## 4 Relationship between Different Desirable Objectives
Minimization of end-to-end delay is desirable but it conflicts other desirable objectives including minimization of packet error rate, maximization of throughput, minimization of overall cost and QoS assurance. 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. QoS implementation is the desirable objective which conflicts with network/battery lifetime, delay and the overall cost of the wireless sensor networks. For example in [138], the authors have proposed a multi-objective routing strategy to find the trade-off between QoS and maximizing the network lifetime. A QoS aware geographic opportunistic routing protocol has been proposed as a multi-objective formulation to optimize QoS and end-to-end delay in wireless sensor networks [155]. In sensor network optimization formulations, maximization of network/battery lifetime is a desirable objective which conflicts with maximization of coverage, maximization of throughput, minimization of packet error rate and QoS. Whereas prolonging the lifetime is supported by energy efficiency and cost minimization objectives. Network/battery lifetime has no direct relationship with the minimization of delay. Energy optimized routing protocol based on clustering has been proposed in [156,157] to maximize the network lifetime and maximize the coverage. The authors have used multi-objective particle swarm optimization algorithm to find the trade-off between the two conflicting objectives. A multi-objective routing protocol has been considered in [158,159] to simultaneously optimize the two conflicting objectives namely, network lifetime and end-to-end delay. Maximization of energy efficiency conflicts with the objective of maximization of throughput whereas it supports the objectives of minimizing cost and maximizing the network/battery lifetime. For example, energy efficiency and network lifetime have been jointly optimized in [160] by considering a multi-objective hybrid routing algorithm for wireless sensor network. Whereas, energy efficiency is design dependent with respect to packet error rate, QoS, coverage and end-to-end delay. Finally, the minimization of overall cost is the ultimate objective of any network operator but it conflicts with many performance parameters of the network for example QoS, coverage maximization, delay minimization, throughput maximization and packet error rate minimization. Whereas, it is supported by the objectives of network/battery lifetime maximization and energy efficiency maximization. The problem of cost minimization and coverage maximization has been formulated as multi-objective optimization of sensor node deployment in [161]. Similarly in [162], the conflicting objectives of cost minimization and delay minimization have been jointly optimized by using a routing algorithms for hop count based forwarding in WSNs.