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 oper ated 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(l) 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.

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.