In our first paper entitled "On the planning of wireless sensor networks: energy efficient clustering under the joint routing and coverage constraint", and published in IEEE Transactions on Mobile Computing, we address the optimal planning of sensors' states in cluster-based sensor networks. In that paper, we consider that any sensor can be in one of three states: active (turned on), turned off or cluster head, and a different power consumption is associated with each of these states. Our objective is to find an energy-optimal topology that maximizes network lifetime while ensuring simultaneously full area coverage and a fully clustered configuration in which the set of cluster heads is constrained to form a spanning tree used as a routing topology. We first formulated this problem as an Integer Linear Programming model that we proved NP-Complete. Then, we implemented a Tabu search heuristic to tackle the exponentially-increasing computation time of the exact resolution. Experimental results show that the proposed heuristic provides near-optimal network lifetime values within low computation times, which in practice is suitable for large-sized sensor networks. Our heuristic also offers better performance than other recent distributed clustering algorithms proposed in the scientific literature. In the second paper entitled "Energy-efficient clustering with transmit power assignment under coverage constraint in wireless sensor networks", submitted to the Wireless Communications and Mobile Computing journal (Wiley), we address the problem of maximizing network lifetime in a WSN, under coverage and clustering constraints. We consider sensors equipped with multiple transmit powers and thus capable of reaching a wider set of cluster heads at higher energy costs, and we propose to find the optimal assignment of states and transmit powers to sensors. We first model this problem as an Integer Linear Programming (ILP) optimisation problem that we prove NP-complete. Then, we propose a Tabu search heuristic to find acceptable-quality solutions within realistic processing times. Our simulation-based results show that our proposed heuristic provides better solutions compared to the optimal solutions generated by CPLEX, i.e., it outperforms, in terms of network lifetime, HEED—the well-recognized distributed clustering algorithm proposed in the literature. In our third paper entitled "A distributed energy-efficient clustering protocol for wireless sensor networks", published in the Computers and Electrical Engineering journal (Elsevier), we propose a new distributed clustering algorithm for WSN, called EECF. The novelty of EECF resides in the relevance of the information gathered by each sensor from its neighbors. Each sensor makes its decision to promote itself cluster head based on score functions exchanged with its one-hop neighbors, which hold information on the ability of its neighbors to become cluster heads. Simulation results show that our algorithm generates better results, in terms of network lifetime, than other distributed algorithms proposed in the recent scientific literature. The main contributions of this thesis are: (1) The modeling of the energy-optimal sensor state planning problem in WSN, under the joint coverage, clustering and routing constraint. The exact solutions of this optimization problem represent an upper bound on the network lifetime and can thus serve as a comparison basis for evaluating the performance of other distributed clustering algorithms addressing the same problem, under the same constraints. To the best of our knowledge, no prior work in the literature has proposed such a bound for the global optimal sensor state planning under the same general integrated coverage, routing and clustering constraints. (2) The modeling of the problem of optimal allocation of states and transmit powers to the sensors in a WSN, under joint coverage and clustering constraints. Solutions of this problem provide an upper bound on the network lifetime and can thus serve as a comparison reference to assess the performance of other distributed algorithms addressing the same problem. (3) The proposal of a novel distributed clustering algorithm for WSN, which outperforms, in terms of lifetime of the network, other popular distributed algorithms proposed in the recent scientific literature. Our algorithm is therefore an improvement of the state-of-the-art on distributed cluster formation algorithms for WSN. (Abstract shortened by UMI.)

Maximizing network lifetime and minimizing energy consumption are among the central concerns in the design of applications and protocols for wireless sensor networks (WSN). Clustering has been proven energy-efficient in sensor networks since data processing, filtering routing and relaying are operated only by cluster heads, thus reducing network load and alleviating the bandwidth. In this paper, we propose a novel distributed clustering algorithm where cluster heads are elected following a three-way message exchange between each sensor and its neighbors. Sensor's eligibility to be elected cluster head is mainly based on its residual energy and its degree. Our protocol has a message exchange complexity of O(1) and a worst-case convergence time complexity of O(N). Simulations show that our algorithm outperforms EESH, a recently published distributed clustering algorithm, in terms of network lifetime and ratio of elected cluster heads.

Minimizing energy dissipation and maximizing network lifetime are important issues in the design of applications and protocols for sensor networks. Energy-efficient sensor state planning consists in finding an optimal assignment of states to sensors in order to maximize network lifetime. For example, in area surveillance applications, only an optimal subset of sensors that fully covers the monitored area can be switched on while the other sensors are turned off. In this paper, we address the optimal planning of sensors' states in cluster-based sensor networks. Typically, any sensor can be turned on, turned off, or promoted cluster head, and a different power consumption level is associated with each of these states. We seek an energy-optimal topology that maximizes network lifetime while ensuring simultaneously full area coverage and sensor connectivity to cluster heads, which are constrained to form a spanning tree used as a routing topology. First, we formulate this problem as an Integer Linear Programming model that we prove NP-Complete. Then, we implement a Tabu search heuristic to tackle the exponentially increasing computation time of the exact resolution. Experimental results show that the proposed heuristic provides near-optimal network lifetime values within low computation times, which is, in practice, suitable for large-sized sensor networks.

This paper presents an architecture based on mobile agents to search for admissible prices of a specific item among many merchant sites over the Internet. To make the search more efficient, mobile agents collaborate by exchanging information. In this paper, we present and compare two collaboration strategies: blackboard and inter-agent communication. The performance of the presented strategies was investigated regarding the response time and the induced network traffic. For that, we carried out analytical and experimental estimation of the lower and the upper bounds of the average search time and average induced network flow of each proposed algorithm. We also addressed system scalability with respect to the number of involved agents. Experimental results show that collaboration among agents improves performance compared to non-collaborative solutions and that increasing the number of agents does not always guarantee better performance.

Maximizing network lifetime and minimizing energy consumption and are two central issues in the design wireless sensor networks (WSN) protocols. In this paper, we address energy-efficient state assignment to sensors in cluster-based WSN, under the constraint of full coverage of the area the sensors are deployed in and connectivity of cluster heads. We consider that any sensor can be turned on, turned off or promoted cluster head, each of these states having a predefined power consumption level.We propose a sensor state assignment heuristic that processes an energy-efficient sensor configuration where every sensor is connected to a cluster head. Besides, we constraint any admissible configuration to have all its cluster heads forming a spanning tree used as a logical routing topology. First, we formulate this global problem as an Integer Linear Programming model that we prove NP-Complete. Then, we implement a greedy heuristic and we show that, compared to its lower bound, this heuristic provides quite good network lifetime values while performing low computation times, practically suitable for large-sized sensor networks.

In this paper, we address the problem of maximizing sensor networks lifetime under area coverage constraint. For that, we propose a scheduling mechanism that calculates, for every time slot of the network operating period, an optimal covering subset of sensors that will be activated while all other sensors will go on Sleep. This mechanisms aims to balance energy dissipation over sensors, thus maximizing network lifetime. We model this problem as an Integer Linear Programming (ILP) problem, that we resolve using ILOG CPLEX and we show that the obtained solutions provide for more balanced energy consumption when we increase a balancing exponent lambda, which increases network lifetime. We finally propose a greedy heuristic that could be implemented to tackle the exponentially-increasing processing time of CPLEX.

Wireless sensor networks consist of a large number of low-power, limited-processing capability sensors that are deployed in afield to measure physical variables or detect events like intrusion or movement. In surveillance applications, ensuring a high area coverage is an important issue requiring a certain number of sensors to be turned on to report events. In this paper, we address the problem of maximizing the sensor network lifetime under area coverage constraint. For that, we propose a scheduling mechanism that calculates, for every time slot of the network operating period, an optimal covering subset of sensors that will be activated while all other sensors will go on sleep. We model this problem as an integer linear programming (ILP) problem, that we resolve using ILOG CPLEX [9] and we show that the obtained solutions provide for a more balanced energy consumption when we increase a balancing exponent lambda, which increases network lifetime.

Wireless sensor networks consist of a large number of sensors equipped with limited energy and processing capabilities. They are deployed in a field to measure physical variables or detect events. In critical surveillance applications, sensors are used to monitor a geographical area and usually a full, or high, area coverage is required as a Quality-of-Service (QoS) parameter. In dense networks, sensors detection ranges usually overlap. Therefore, only a covering subset of sensors can be turned on while other sensors are put in a very low-power Sleep state. In this paper, we address the problem of maximizing the sensor network lifetime under area coverage constraint. For that, we propose a mechanism that dynamically activates an optimal covering subset of sensors, based on residual energies. We first model this problem as an Integer Linear Programming (ILP) problem that we resolve using CPLEX. Then, we propose a greedy heuristic to tackle the exponentially-increasing processing times of the exact solution. We show that the proposed heuristic provides for acceptable solutions while having a polynomial O(N2) complexity, suitable for large-scale networks.

This paper presents an architecture based on mobile agent technology for retrieving prices of a specific item among many distributed merchant sites. We introduce two collaboration strategies that we implemented and tested: communication through blackboard and inter-agent communication. Empirical performance study was made, based on measurements of response time and induced network load, in the best and worst case scenarios. We also tested the scalability of the system with respect to the number of involved agents. Results show that collaboration between agents gives better performance than non-collaboration and each collaboration strategy is recommended for particular specifications.