Network lifetime maximising distributed forwarding strategies in Ad Hoc wireless sensor networks

Abstract

Abstract:The authors propose three variants of distributed and stateless forwarding strategies for wireless sensor networks, namely greedy minimum energy consumption forwarding protocol (GMFP), lifetime maximising GMFP (LM-GMFP) and variance minimising GMFP (VAR-GMFP), which aim at maximising the network lifetime while achieving a high forwarding success rate. GMFP selects a forwarding node that minimises per-packet energy consumption while maximising the forwarding progress. LM-GMFP extends the GMFP algorithm by also taking into account the remaining energy at the prospective one-hop forwarding nodes. In VAR-GMFP, on the other hand, the packet is forwarded to the next node that ensures a locally high mean and low variance of nodal remaining energy. Through simple probabilistic analysis the authors prove the intuition behind the optimum forwarding node selection for network lifetime maximisation. They then model the lifetime maximisation of a sensor network as an optimisation problem and compare the practical protocol-dependent network lifetime with the theoretical upper bound. Through extensive simulations the author demonstrate that the proposed protocols outperform the existing energy-aware protocols in terms of network lifetime and end-to-end delay.

Multiobjective Optimization for a Wireless Ad Hoc Sensor Distribution on Shaped-Bounded Areas

“…The use of (16) This is (16) of Section 3 assuming = and is different from the value of 3 given in [61].…”

“…(30)) if needed; (12) while a tree is not obtained do (13) Validate ; (14) Update ( , ) and radius (Eq. (30)) if needed; (15) Apply MST Prim-Dijkstra to array of links; (16) if some nodes are not connected then (17) Update position and radius (Eq. (30)) of unlinked nodes; (18) else nodes are connected (19) Tree has been built; (20) end if (21) end while (22) Apply selection operator (Eq.…”

“…This figure is used to obtain (14) and (16), which are used to obtain (16), which is the area of the circular triangle. The vertices of the circular triangle are labeled by ( , ).…”

“…It is worth mentioning that sensor distribution with minimum energy connectivity is an NP-complete problem [9][10][11][12]. In the literature, different strategies for wireless ad hoc and sensor networks related to topology control [2], node localization [13,14], distributed coverage [15], and network lifetime [16] are described to provide solutions for sensor distribution, coverage area, and energy consumption problems. Additional related problems in wireless communication such as frequency allocation and interference have been also solved by bioinspired algorithms as it is reported in [17,18] or in [19], where the authors show an efficient way of solving the minimum number of hops among nodes to be communicated.…”

Wireless Sensor Network Optimization: Multi-Objective Paradigm

“…Whereas minimization of delay has design dependent relationship with network/battery lifetime, coverage and energy efficiency. Trade-off between end-to-end delay and energy conservation has been achieved in [ 153 ] by using heuristic optimization approach, called variance minimizing greedy minimum energy consumption forwarding protocol. A multi-objective optimization based routing scheme for wireless sensor networks has been proposed in [ 154 ] to optimize end-to-end delay, reliability, jitter, interference and energy consumption.…”