minimizing energy consumption in wireless sensor networks using solar powered sensors

Minimizing Energy Consumption in Wireless Sensor Networks using Solar Powered sensors

Maryam El azhari, Rachid Latif, Ahemd Toumanari Signals Systems and Computer Sciences Group (ESSI), National School of Applied Sciences, Ibnou Zohr University,

Agadir Morocco

In the last years, wireless sensor networks (WSNs) have gained increasing attention from both research community and actual users. Since sensor nodes are battery-powered devices, the main issue to resolve is to minimize the energy consumption of nodes so that the network lifetime can be extended to reasonable periods of time. Several algorithms were proposed to meet strict energy saving requirements which may be divided into: Location; Data-centric; Mobility; Multipath; Heterogeneity; Qos and other specific criterions. However, those protocols based on the client-server concept don’t actually consider the fact that a sensor node has to afford various capabilities for multiple applications. That’s why researches introduced the Mobile Agent (MA) approach which is a special kind of software that migrates among network nodes to carry out tasks autonomously and intelligently. The Mobile Agent saves a great amount of energy but still presents much more latency compared with the client–server approach. Looking for alternative energy source seems efficient for prolonging the network lifetime moreover, solar energy has become more attractive recently because of its environmental benefits and because the efficiency of photovoltaic cells has increased significantly in the past few years. That’s why, in this work, we tried to find a trade-off balance between the energy consumption and task latency using both solar powered nodes and Mobile Agents and we presented afterwards an improved version of the Balanced Itinerary Planning for Multiple Mobile Agents (BST-MIP) algorithm based on solar powered sensors as alternative energy source in order to highlight the pros of our new approach.

Keywords: Wireless sensor networks; energy efficiency; routing protocols; solar sensors; mobile agents

1. Introduction

Wireless sensor network (WSN) is widely considered as one of the most important technologies for the twenty-first century, it typically consists of a large number of low-cost, low-power, and multifunctional wireless sensor nodes, with sensing, wireless communications and computation capabilities [1]. The WSN nodes are designed to be battery-operated, since they may be utilized in any kind of environment including thick forestry, volcanic mountains and oceanbeds. Consequently, everything must be designed to be power-aware in these networks. These sensor nodes communicate over short distance via a wireless medium and collaborate to accomplish a common task, for example, environment monitoring, military surveillance, and industrial process control. Energy savings is a top concern for wireless sensor networks [2]. Sensors dissipate energy over sensing, processing and communication tasks and the network lifetime is basically dependable on the power consumption. Generally, the lifetime of WSNs is correlated with the battery current usage profile. A better management of battery power leads to a better consumption of applications and routing protocols in order to increase the network lifetime. Design constraints are application dependent and are based on the monitored environment. The environment plays a key role in determining the size of the network, the deployment scheme, and the network topology. The size of the network varies with the monitored environment. For indoor environments, fewer nodes are required to form a network in a limited space whereas outdoor environments may require more nodes to cover a larger area. An ad hoc deployment is preferred over pre-planned deployment when the environment is inaccessible by humans or when the network is composed of hundreds to thousands of nodes. Obstructions in the environment can also limit communication between nodes, which in turn affects the network connectivity. Current WSNs are deployed on land, underground, and underwater. Depending on the environment, a sensor network faces different challenges and constraints. There are five types of WSNs: terrestrial WSN, underground WSN, underwater WSN, multi-media WSN, and mobile WSN [3-4].

2. Sensor Networks Applications

Low cost and Miniaturization of sensor nodes has allowed invading several areas of applications. They also help to extend the scope of existing applications. Among these areas where these networks are very useful and can provide better contributions, one can note the military, health, environmental and smart homes. Fig. 1 enumerates examples of sensor networks applications and we give below more details on each type of application [5].

2.1 Military Applications

The low cost, rapid deployment, self-organization and fault tolerance are characteristics that have made effective sensor networks for military applications. Several projects were launched to support military units in the battlefield and protect

Materials and processes for energy: communicating current research and technological developments (A. Méndez-Vilas, Ed.)____________________________________________________________________________________________________

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cities against attacks, such as terrorist threats. The project DSN (Distributed Sensor Network) at DARPA (Defense Advanced Research Projects Agency) was one of the first projects in the 80's used with sensor networks to gather distributed data. Researchers at Lawrence Livermore National Laboratory have developed the network WATS (Wide Area Tracking System). This network consists of detectors of gamma rays and neutrons to detect nuclear screen devices. It consists of constant surveillance of a specific area. It uses techniques of aggregation of data for reporting to a central intelligence. The researchers then set up another network called JBREWS (Joint Biological Remote Early Warning System) to warn the troops in the field of possible biological attacks. A sensor network can be deployed in a strategic or hostile, to monitor the movements of enemy forces, or analyze the field before sending troops (detection of chemical, biological or radiation). The U.S. military has conducted tests in the California desert.

2.2 Survey applications

The application of sensor networks in the security field can significantly reduce financial expenditures devoted to securing places and people. Thus, the integration of sensors in large structures like bridges or buildings will help to detect cracks and alterations in the structure following an earthquake or an aging structure. The deployment of a network of motion sensors can be an alarm system that will be used to detect intrusions in a surveillance zone.

2.3 Environmental applications

The term Environmental Sensor Network has evolved to cover many applications of WSNs to earth science research. This includes sensing volcanoes, oceans, glaciers, forests, etc. Some other major areas are listed below.

2.3.1 Air pollution monitoring

Wireless sensor networks have been deployed in several cities (Stockholm, London or Brisbane) to monitor the concentration of dangerous gases for citizens. These can take advantage of the ad-hoc wireless links rather than wired installations, which also make them more mobile for testing readings in different areas. There are various architectures that can be used for such applications as well as different kinds of data analysis and data mining that can be conducted.

2.3.2 Forest fires detection

A network of Sensor Nodes can be installed in a forest to detect when a fire has started. The nodes can be equipped with sensors to measure temperature, humidity and gases which are produced by fires in the trees or vegetation. The early detection is crucial for a successful action of the fire-fighters; thanks to Wireless Sensor Networks, the fire brigade will be able to know when a fire is started and how it is spreading.

2.3.3 Greenhouse monitoring

Wireless sensor networks are also used to control the temperature and humidity levels inside commercial greenhouse. When the temperature and humidity drops below specific levels, the greenhouse manager must be notified via e-mail or cell phone text message, or host systems can trigger misting systems, open vents, turn on fans, or control a wide variety of system responses.

2.3.4 Landslide detection

A landslide detection system makes use of a wireless sensor network to detect the slight movements of soil and changes in various parameters that may occur before or during a landslide. And through the data gathered it may be possible to know the occurrence of landslides long before it actually happens.

2.4 Medical applications

In the field of medicine, sensor networks can be used to ensure continuous monitoring of vital organs of the human being through micro-sensors that can be swallowed or implanted under the skin (blood glucose monitoring, detection cancers...). They can also help diagnose some diseases by performing physiological measurements such as blood pressure, heart beat. The physiological data collected by the sensors can be stored for a long time to track a patient. Other hand, these networks can detect abnormal behavior (fall of a bed, shock, cry ...) in dependent people (disabled or elderly).

2.5 Home automation system

With technological development, the sensors can be embedded in devices such as vacuum cleaners, microwave ovens, refrigerators, VCRs... These embedded sensors can interact with each other and an external network via the Internet to enable a user to control home appliances locally or remotely. The deployment motion sensors and temperature in the future so-called smart homes can automate several domestic operations such as the lights dim and the music goes into a


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shutdown state when the chamber is empty, air conditioning and heating according to adjust multiple points of measurement, the triggering of an alarm by the anti-intrusion sensor when an intruder wants to access at home.

Fig. 1 WSNs applications

3. Routing protocols in WSNs

Routing in wireless sensor networks differs from conventional routing in fixed networks in various ways. There is no infrastructure, wireless links are unreliable, sensor nodes may fail, and routing protocols have to meet strict energy saving requirements. Many routing algorithms were developed for wireless networks in general. All major routing protocols proposed for WSNs may be divided into seven categories as shown in table 1. We review sample routing protocols in each of the categories in preceding sub-sections [6]. Table 1 Routing Protocols for WSNs.

Category Representative Protocols Location-based Protocols MECN, SMECN, GAF, GEAR, Span, TBF, BVGF, GeRaF Data-centric Protocols SPIN, Directed Diffusion, Rumor Routing, COUGAR, ACQUIRE, EAD,

Information-Directed Routing, Gradient-Based Routing, Energy-aware Routing, Information-Directed Routing, Quorum-Based Information Dissemination, Home Agent Based Information Dissemination

Hierarchical Protocols LEACH, PEGASIS, HEED, TEEN, APTEEN Mobility-based Protocols SEAD, TTDD, Joint Mobility and Routing, Data MULES, Dynamic Proxy Tree-

Base Data Dissemination Multipath-based Protocols Sensor-Disjoint Multipath, Braided Multipath, N-to-1

Multipath Discovery Heterogeneity-based Protocols

IDSQ, CADR, CHR

QoS-based protocols SAR, SPEED, Energy-aware routing

3.1 Location-based Protocols

In location-based protocols, sensor nodes are addressed by means of their locations. Location information for sensor nodes is required for sensor networks by most of the routing protocols to calculate the distance between two particular nodes so that energy consumption can be estimated. A sample of location-aware routing protocols proposed for WSNs is presented below:

3.1.1 Geographic Adaptive Fidelity (GAF)

GAF is an energy-aware routing protocol primarily proposed for MANETs, but can also be used for WSNs because it favors energy conservation. The design of GAF is motivated based on an energy model that considers energy consumption due to the reception and transmission of packets as well as idle (or listening) time when the radio of a


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sensor is on to detect the presence of incoming packets. GAF is based on mechanism of turning off unnecessary sensors while keeping a constant level of routing fidelity (or uninterrupted connectivity between communicating sensors). In GAF, sensor field is divided into grid squares and every sensor uses its location information, which can be provided by GPS or other location systems, to associate itself with a particular grid in which it resides. This kind of association is exploited by GAF to identify the sensors that are equivalent from the perspective of packet forwarding.

3.1.2 Geographic and Energy-Aware Routing (GEAR)

GEAR is an energy-efficient routing protocol proposed for routing queries to target regions in a sensor field. In GEAR, the sensors are supposed to have localization hardware equipped, for example, a GPS unit or a localization system so that they know their current positions. Furthermore, the sensors are aware of their residual energy as well as the locations and residual energy of each of their neighbors. GEAR uses energy aware heuristics that are based on geographical information to select sensors to route a packet toward its destination region. Then, GEAR uses a recursive geographic forwarding algorithm to disseminate the packet inside the target region.

3.1.3 Coordination of Power Saving with Routing

Span is a routing protocol also primarily proposed for MANETs, but can be applied to WSNs as its goal is to reduce energy consumption of the nodes. Span is motivated by the fact that the wireless network interface of a device is often the single largest consumer of power. Hence, it would be better to turn the radio off during idle time. Although Span does not require that sensors know their location information, it runs well with a geographic forwarding protocol. Span helps sensors to join a forwarding backbone topology as coordinators that will forward packets on behalf of other sensors between any source and destination. When used with a geographic forwarding protocol, Span's election rule requires each sensor to advertise its status (i.e., coordinator or non-coordinator), its neighbors, and its coordinators. Furthermore, when it receives a packet, a coordinator forwards the packet to a neighboring coordinator if any, which is the closest to the destination or to a non-coordinator that is closer to the destination.

3.2 Data Centric Protocols

Data-centric protocols differ from traditional address-centric protocols in the manner that the data is sent from source sensors to the sink. In address-centric protocols, each source sensor that has the appropriate data responds by sending its data to the sink independently of all other sensors. However, in data-centric protocols, when the source sensors send their data to the sink, intermediate sensors can perform some form of aggregation on the data originating from multiple source sensors and send the aggregated data toward the sink. This process can result in energy savings because of less transmission required to send the data from the sources to the sink. We review some of the data-centric routing protocols for WSNs below:

3.2.1 Sensor Protocols for Information via Negotiation (SPIN)

SPIN protocol was designed to improve classic flooding protocols and overcome the problems they may cause, for example, implosion and overlap. The SPIN protocols are resource aware and resource adaptive. The sensors running the SPIN protocols are able to compute the energy consumption required to compute, send, and receive data over the network. Thus, they can make informed decisions for efficient use of their own resources. The SPIN protocols are based on two key mechanisms namely negotiation and resource adaptation. SPIN enables the sensors to negotiate with each other before any data dissemination can occur in order to avoid injecting non-useful and redundant information in the network. SPIN uses meta-data as the descriptors of the data that the sensors want to disseminate. The notion of meta-data avoids the occurrence of overlap given sensors can name the interesting portion of the data they want to get. It may be noted here that the size of the meta-data should definitely be less than that of the corresponding sensor data. Contrary to the flooding technique, each sensor is aware of its resource consumption with the help of its own resource manager that is probed by the application before any data processing or transmission. This helps the sensors to monitor and adapt to any change in their own resources.

3.2.2 Directed Diffusion

Directed diffusion is a data-centric routing protocol for sensor query dissemination and processing. It meets the main requirements of WSNs such as energy efficiency, scalability, and robustness. Directed diffusion has several key elements namely data naming, interests and gradients, data propagation, and reinforcement. A sensing task can be described by a list of attribute-value pairs. At the beginning of the directed diffusion process, the sink specifies a low data rate for incoming events. After that, the sink can reinforce one particular sensor to send events with a higher data rate by resending the original interest message with a smaller interval. Likewise, if a neighboring sensor receives this


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interest message and finds that the sender's interest has a higher data rate than before, and this data rate is higher than that of any existing gradient, it will reinforce one or more of its neighbors.

3.2.3 Rumor Routing

Rumor routing is a logical compromise between query flooding and event flooding app schemes routing is an efficient protocol if the number of queries is between the two intersection points of the curve of rumor routing with those of query flooding and event flooding. Rumor routing is based on the concept of agent, which is a long-lived packet that traverses a network and informs each sensor it encounters about the events that it has learned during its network traverse. An agent will travel the network for a certain number of hops and then die. Each sensor, including the agent, maintains an event list that has event-distance pairs, where every entry in the list contains the event and the actual distance in the number of hops to that event from the currently visited sensor. Therefore, when the agent encounters a sensor on its path, it synchronizes its event list with that of the sensor it has encountered. Also, the sensors that hear the agent update their event lists according to that of the agent in order to maintain the shortest paths to the events that occur in the network.

3.3 Hierarchical Protocols

Hierarchical routing protocols [7] have proved to have considerable savings in total energy consumption of the WSN. In hierarchical routing protocols (Fig. 2), clusters are created and a head node is assigned to each cluster. The head nodes are the leaders of their groups having responsibilities like collection and aggregation the data from their respective clusters and transmitting the aggregated data to the BS. This data aggregation in the head nodes greatly reduces energy consumption in the network by minimizing the total data messages to be sent to BS. The less the energy consumption, the more the network life time. The main idea of developing cluster-based routing protocols is to reduce the network traffic toward the sink. This method of clustering may introduce overhead due to the cluster configuration and maintenance, but it has been demonstrated that cluster-based protocols exhibit better energy consumption and performance when compared to flat network topologies for large-scale WSNs.

3.3.1 Challenges of clustering

Wireless Sensor Networks present vast challenges in terms of implementation. There are several key attributes that designers must carefully consider which are of particular importance in wireless sensor networks: Cost of Clustering, Selection of Cluster heads and Clusters, Real-Time Operation, Synchronisation, Data Aggregation, Repair Mechanisms and Quality of Service (QoS).

Fig. 2 Cluster-based Hierarchical Model

3.3.2 Power-Efficient Gathering in Sensor Information Systems (PEGASIS)

PEGASIS is an extension of the LEACH protocol, which forms chains from sensor nodes so that each node transmits and receives from a neighbor and only one node is selected from that chain to transmit to the base station (sink). The data is gathered and moves from node to node, aggregated and eventually sent to the base station. The chain construction is performed in a greedy way. Unlike LEACH, PEGASIS avoids cluster formation and uses only one node


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in a chain to transmit to the BS (sink) instead of using multiple nodes. A sensor transmits to its local neighbors in the data fusion phase instead of sending directly to its CH as in the case of LEACH. In PEGASIS routing protocol, the construction phase assumes that all the sensors have global knowledge about the network, particularly, the positions of the sensors, and use a greedy approach. When a sensor fails or dies due to low battery power, the chain is constructed using the same greedy approach by bypassing the failed sensor. In each round, a randomly chosen sensor node from the chain will transmit the aggregated data to the BS, thus reducing the per round energy expenditure compared to LEACH. Simulation results showed that PEGASIS is able to increase the lifetime of the network twice as much the lifetime of the network under the LEACH protocol. Such performance gain is achieved through the elimination of the overhead caused by dynamic cluster formation in LEACH and through decreasing the number of transmissions and reception by using data aggregation. Although the clustering overhead is avoided, PEGASIS still requires dynamic topology adjustment since a sensor node needs to know about energy status of its neighbors in order to know where to route its data. Such topology adjustment can introduce significant overhead especially for highly utilized networks.

3.3.3 Threshold Sensitive Energy Efficient Sensor Network Protocol (TEEN)

TEEN is a hierarchical clustering protocol, which groups sensors into clusters with each led by a CH. The sensors within a cluster report their sensed data to their CH. The CH sends aggregated data to higher level CH until the data reaches the sink. Thus, the sensor network architecture in TEEN is based on a hierarchical grouping where closer nodes form clusters and this process goes on the second level until the BS (sink) is reached. TEEN is useful for applications where the users can control a trade-off between energy efficiency, data accuracy, and response time dynamically. TEEN uses a data-centric method with hierarchical approach. Important features of TEEN include its suitability for time critical sensing applications. Also, since message transmission consumes more energy than data sensing, so the energy consumption in this scheme is less than the proactive networks. However, TEEN is not suitable for sensing applications where periodic reports are needed since the user may not get any data at all if the thresholds are not reached.

3.4 Mobility-based Protocols

Mobility brings new challenges to routing protocols in WSNs. Sink mobility requires energy efficient protocols to guarantee data delivery originated from source sensors toward mobile sinks. In this section we discuss sample mobility-based routing protocols for mobile WSNs.

3.4.1 Joint Mobility and Routing Protocol

A network with a static sink suffers from a severe problem [8], called energy sink-hole problem, where the sensors located around the static sink are heavily used for forwarding data to the sink on behalf of other sensors. As a result, those heavily loaded sensors close to the sink deplete their battery power more quickly, thus disconnecting the network. This problem exists even when the static sink is located at its optimum position corresponding to the center of the sensor field .To address this problem, a mobile sink for gathering sensed data from source sensors was suggested, it consists of making the sensors surrounding the sink change over time, giving the chance to all sensors in the network to act as data relays to the mobile sink and thus balancing the load of data routing on all the sensors. Under the shortest-path routing strategy, the average load of data routing is reduced when the trajectories of the sink mobility correspond to concentric circles (assuming that the sensor field is a circle). Another category of mobility trajectories is to move the sink in annuli. However, such movement can be viewed as a weighted average over the movements on a set of concentric circles. In particular, the optimum mobility strategy of the sink is a symmetric strategy in which the trajectory of the sink is the periphery of the network. The trajectory with a radius equal to the radius of the sensor field maximizes the distance from the sink to the centre of the network that represents the hot spot.

3.4.2 Scalable Energy-Efficient Asynchronous Dissemination (SEAD)

SEAD is self-organizing protocol, which was proposed to trade-off between minimizing the forwarding delay to a mobile sink and energy savings. SEAD considers data dissemination in which a source sensor reports its sensed data to multiple mobile sinks and consists of three main components namely dissemination tree (d-tree) construction, data dissemination, and maintaining linkages to mobile sinks. It assumes that the sensors are aware of their own geographic locations. Every source sensor builds its data dissemination tree rooted at itself and all the dissemination trees for all the source sensors are constructed separately. SEAD can be viewed as an overlay network that sits on top of a location-aware routing protocol, for example, geographical forwarding.

3.4.3 Dynamic Proxy Tree-Based Data Dissemination

A dynamic proxy tree-based data dissemination framework was proposed for maintaining a tree connecting a source sensor to multiple sinks that are interested in the source. This helps the source disseminate its data directly to those


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mobile sinks. In this framework, a network is composed of stationary sensors and several mobile hosts, called sinks. The sensors are used to detect and continuously monitor some mobile targets, while the mobile sinks are used to collect data from specific sensors, called sources, which may detect the target and periodically generate detected data or aggregate detected data from a subset of sensors. Because of target mobility, a source may change and a new sensor closer to the target may become a source. Each source is represented by a stationary source proxy and each sink is represented by a stationary sink proxy. The source and sink proxies are temporary in the sense that they change as the source sensors change and the sinks move. A source will have a new source proxy only when the distance between the source and its current proxy exceeds a certain threshold. Likewise, a sink will have a new sink proxy only when the distance between the sink and its current proxy exceeds a certain threshold. The design of such proxies reduces the cost of pushing data to and querying data from the source and sinks proxies.

3.5 Heterogeneity-based Protocols

In heterogeneity sensor network architecture, there are two types of sensors namely line-powered sensors which have no energy constraint, and the battery-powered sensors having limited lifetime, and hence should use their available energy efficiently by minimizing their potential of data communication and computation. In this section we discuss uses of heterogeneity in WSNs to extend network lifetime and present a few routing protocols.

3.5.1 Information-Driven Sensor Query (IDSQ)

IDSQ addresses the problem of heterogeneous WSNs of maximizing information gain and minimizing detection latency and energy consumption for target localization and tracking through dynamic sensor querying and data routing. To improve tracking accuracy and reduce detection latency, communication between sensors is necessary and consumes significant energy. In order to conserve power, only a subset of sensors need to be active when there are interesting events to report in some parts of the network. The choice of a subset of active sensors that have the most useful information is balanced by the communication cost needed between those sensors. Useful information can be sought based on predicting the space and time interesting events would take place. In IDSQ protocol, first step is to select a sensor as leader from the cluster of sensors. This leader will be responsible for selecting optimal sensors based on some information utility measure.

3.5.2 Cluster-Head Relay Routing (CHR)

CHR routing protocol uses two types of sensors to form a heterogeneous network with a single sink: a large number of low-end sensors, denoted by L-sensors, and a small number of powerful high-end sensors, denoted by H-sensors. Both types of sensors are static and aware of their locations using some location service. Moreover, those Land H-sensors are uniformly and randomly distributed in the sensor field. The CHR protocol partitions the heterogeneous network into groups of sensors (or clusters), each being composed of L-sensors and led by an H-sensor. Within a cluster, the L-sensors are in charge of sensing the underlying environment and forwarding data packets originated by other L-sensors toward their cluster head in a multihop fashion. The H-sensors, on the other hand, are responsible for data fusion within their own clusters and forwarding aggregated data packets originated from other cluster heads toward the sink in a multihop fashion using only cluster heads. While L-sensors use short-range data transmission to their neighboring H-sensors within the same cluster, H-sensors perform long-range data communication to other neighboring H-sensors and the sink.

3.6 QoS-based Protocols

In addition to minimizing energy consumption, it is also important to consider quality of service (QoS) requirements in terms of delay, reliability, and fault tolerance in routing in WSNs. In this section, we review a sample QoS based routing protocols that help find a balance between energy consumption and QoS requirements.

3.6.1 Energy-Aware QoS Routing Protocol

In this QoS aware protocol for sensor networks, realtime traffic is generated by imaging sensors. The proposed protocol finds a least cost and energy efficient path that meets certain end-to-end delay during the connection. The link cost used is a function that captures the nodes’ energy reserve, transmission energy, error rate and other communication parameters. In order to support both best effort and real-time traffic at the same time, a class-based queuing model is employed. The queuing model allows service sharing for real-time and non-real-time traffic. The protocol finds a list of least cost paths by using an extended version of Dijkstra’s algorithm and picks a path from that list which meets the end-to-end delay requirement. Simulation results show that the proposed protocol consistently performs well with respect to QoS and energy metrics; however, it does not provide flexible adjusting of bandwidth sharing for different links.


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4. Mobile Agent

The application-specific nature of tasking a wireless sensor network (WSN) requires that sensor nodes have various capabilities for multiple applications. It would be impractical to store in the local memory of embedded sensors the entire program needed to run every possible application, due to the tight memory constraints. A Mobile Agent (MA) is a special kind of software that migrates among network nodes to carry out task(s) autonomously and intelligently in response to changing conditions in the network environment, in order to achieve the objectives of the agent dispatcher [9]. There are seven good reasons for using Mobile agents: Secure Brokering distributed information retrieval, parallel processing information dissemination, monitoring and notification, workflow applications and groupeware, telecommunication networks services [10]. The use of MAs to dynamically deploy new applications in WSNs has been proven to be an effective method to address this challenge. Recently there has been a growing interest on the design, development, and deployment of MA systems for high-level inference and surveillance in WSNs. The agent design in WSNs is decomposed into four components, i.e., architecture, itinerary planning, middleware system design and agent cooperation. Among the four components, itinerary planning determines the order of source nodes to be visited during agent migration, which has a significant impact on energy performance of the MA system. It has been shown that finding an optimal itinerary is NP-hard. Therefore, heuristic algorithms are generally used to compute competitive itineraries with a sub-optimal performance.

5. Mobile Agent Itinerary Planning

Compared to conventional data fusion in wireless sensor networks, MA system is better due to its intrinsic flexibility through migration of MA. In addition, it has been shown that data compression and fusion using MAs achieve better energy efficiency. However, using MA also introduces larger task latency while MA immigrates across a network with a large number of source nodes. To address this issue, a multi-agent system is proposed to achieve a balanced trade-off between energy cost and task latency. In a multiple mobile software agents system, serval MAs roams in the network simultaneously. Each MA visits a subset of source nodes to retrieve information for the sink. In contrary to single MA itinerary planning (SIP) [11], it becomes more challenging to determine the number of MAs and their corresponding subsets of source nodes for multi-agent itinerary planning (MIP), which is also called source grouping problem To address this issue, the minimum spanning tree (MST) is used to solve the MA grouping problem. The concepts consist on modeling the network topology as a totally connected graph (TCG). In order to simplify the TCG, only source nodes and the sink node constitute its vertices, while the weight of each arc can be basically estimated by the Leung hop count among source nodes or the sink. According to such a hop-count-oriented TCG (H-TCG), a MST is generated, where each branch stemmed from the sink corresponds to a group of sources included in the branch. Then, the number of branches in the MST is equivalent to the number of MAs used in the network, and each agent will collect sensory data by traversing the sources in its corresponding branch before returning to the sink.

6. Using Solar Powered sensors for energy efficiency

Solar energy has become more attractive recently because of its environmental benefits and because the efficiency of photovoltaic cells has increased significantly in the past few years. Solar energy is derived from Nature’s greatest renewable resource and it is non-toxic in nature [12]. Solar cells can be utilized to power the sensor as well as to charge the batteries for WSNs, where their design goal is to provide autonomy lifetime (for wireless sensor nodes), making WSNs more valuable in terms of versatility and longevity. Solar cells are well-developed technology compared to other energy harvesting techniques [13],they are fairely compact, capable of efficiently generating multiple potentials, and compatible with other processors. Two generations of solar cells are distinguished:

6.1 Bulk solar cells

Figure 3.a shows Bulk solar cells, they are better suited to high light conditions and the outdoors spectrum, with efficiency ~ 15% - 20%; the poly-crystalline Si is cheaper, but less efficient than single-crystalline silicon:


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(a) (b)

Fig. 3 Types of solar cells. Bulk solar cells structure (a) and highlights the components of thin-film solar cells (b)

6.2 Thin-film solar cells

Figure 3.b shows thin-film solar cells, the spectral response is more closely matches that of artificial indoor light; amorphous Si presents conversion efficiencies are ~5-10%; CdTe cells with good PV potential that give rise to environmental concern Freie Universität Berlin laboratory team has highlighted the pros of solar powered nodes by using sensor board hardware [14],it consists of a Texas Instruments MSP430 controller as core and a set of associated sensor hardware. These sensors are:

• A light sensor for the detection of visible light, • A passive infrared sensor for detection of movement, • A temperature sensor, • A gravitation sensor for the detection of movement of the sensor board, • A microphone for determination of the ambient noise level.

The sensors shown in Figure 4 communicate via an RF module in the 868 MHz band. The sensor board can be operated in three different power modes. The energy needed even in the most power-intensive mode (ca. 40 mA) can be provided by a solar cell. Figure 4 shows the sensor board and an attached solar cell. Note that much smaller solar cells could be used, that provide enough power in the daylight, but these are more expensive. The first generation of the sensor board has the size of three AAA batteries.

Fig. 4 Sensor board with attached solar cells

7. Previous work on MST-MSP

A novel scheme entitled MST-MIP was proposed based on minimum spanning tree, where each branch stemmed from the sink corresponds to a group of source nodes assigned for a mobile agent to visit. A balancing factor α was introduced to achieve a flexible trade-off control between energy cost and task duration, and the balanced MST-MIP


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algorithm is named BST-MIP. Extensive experiments show that MST-MIP has lower energy consumption than previous MIP proposals [15], while BST-MIP decreases the task duration up to 50%. In the basic MST-MIP algorithm, the hop count of two vertices is directly set to the arc weight in the H-TCG, and the Prim algorithm with greedy feature is used to construct the MST. When the source nodes are close to each other, the relatively small hop count between two adjacent sources easily becomes the shortest edge during the selection of Prim algorithm. Consequently, the longer the branch is, the higher that the possibility to connect more source nodes is which causes the reduction of the number of branches stemmed from the sink.

(a) b)

Fig. 5 MST-MIP schema: problem of MST-MIP (a) and highlights the arc weight updated using BST-MIP (b)

Given the example shown in figure 5.a, The MST contains a single branch, which means only one MA is sent to the network, and the task duration will be as high as in SIP algorithm. To achieve a trade of balance between energy-cost and task duration, a balancing factor is introduced to update the value of each arc as follows:

w=α × h ij + (1 - α) × ( hsi + hs

j ) (1) Where: α ∊ [0, 1], i and j represent indexes of sensor nodes and hi

j is the number of hopes to move from one node i to node j and vice versa. For a value α = 0.6 [10], the weight of each arc is updated as indicated in figure 5.b. In this chapter, we propose an improved version of the BST-MIP algorithm which consists on minimizing the end-to-end delay. The new approach main purpose is to combine the additional energy provided by solar nodes with the mobile agent concept. As mentioned in figure 6.a and figure 6.b , a specific number of solar nodes is deployed in the zone centered at the sink node with radius βRmax determined by the user, where β ∊ [0, 1] is a regulatory factor and Rmax is the maximum radius of the zone. The MA circulates the itinerary to gather data and send it afterwards back to the sink. The agent concept presents much more latency compared with the client server mechanism, so in order to optimize the time delivery, the mobile agent will relay data to the nearest solar node located at the zone around the sink while migrating through its path and then the solar node will hand the data directly to the sink node, this way, the end-to-end delay is minimized and the performance of the algorithm is improved.

(a) (b) Fig. 6 BST-MIP schema with mobile agent concept. Mobile Agent Multiple-Itinerary Planning (a) and Combining solar nodes with mobile agent approach (b)


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8. Conclusion

Sensor Networks are emerging as a great aid in improving the way data gathered. The energy management is one of the important constraints in WSNs because the energy storage is limited in terms of energy. Many methods are presented for energy management and as previously mentioned, deploying multiple MAs enhances the overall performance of of MA systems in WSNs by providing a trade-off between energy consumption and task duration. The previous studies show that solar energy is an efficient alternate energy source for WSNs. The solar energy based routing concepts increase the performance and life of WSNs compared to other conventional routing algorithms. In this chapter, we present an improved version of a balanced minimum spanning tree based on solar powered nodes, and as a future work, we would be working on getting numerical results using NetTopo simulator, but also determining the optimized number of solar nodes deployed around the sink node by using the simplex method in order to reach minimum time delivery.

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