Cross‐layer network lifetime optimisation considering transmit and signal processing power in wireless sensor networks

Abstract

Q2Maintaining high energy efficiency is essential for increasing the lifetime of wireless sensor networks (WSNs), where the battery of the sensor nodes cannot be routinely replaced. Nevertheless, the energy budget of the WSN strictly relies on the communication parameters, where the choice of both the transmit power as well as of the modulation and coding schemes (MCSs) plays a significant role in maximising the network lifetime (NL). In this study, the authors optimise the NL of WNSs by analysing the impact of the physical layer parameters as well as of the signal processing power (SPP) P sp on the NL. They characterise the underlying trade-offs between the NL and bit error ratio (BER) performance for a predetermined set of target signal-to-interference-plus-noise ratio (SINR) values and for different MCSs using periodic transmit-time slot (TS) scheduling in interference-limited WSNs. The NL maximisation is formulated as a non-linear optimisation problem taking into account the lower-bounded SINR, the energy consumption constraint, the maximum transmit power per link and again, periodic transmit-TS scheduling for all active TSs. The non-linear energy consumption constraint encountered is relaxed by employing a change of variables, which converts the problem into a linear form. Hence they can obtain the globally optimal solution of the original problem by solving a linear programming problem with the aid of the interior point method. The author's results demonstrate that for a per-link target BER requirement (PLBR) of 10 −3 , a 'continuous-time' NL of 4.99 years (yr) is achieved by 1/2-rate convolutional coded soft-decoded quadrature phase-shift keying for an additive white Gaussian noise channel, when ignoring the SPP, which is reduced to 0.89 yr because of the SPP. By contrast, the best NL that is achieved by 1/2-rate serially concatenated coding after ten iterations at a PLBR of 10 −3 in a Rayleigh fading channel, which is reduced from 1.55 to 0.58 yr because of the SPP.

CLIF: Cross-Layer Interactive Framework for Enhancing Network Lifetime and Communication in WSN

“…Boubiche et al [14] have used Cross layer approach for securing data aggregation using watermarking technique on heterogeneous WSN. Yetgin et al [15] have presented cross layer approach for enhancing the network lifetime by emphasizing on the physical layers. Similar cross layer methodology was also used in the study of Lin and Kokkinos [16] for solving the hidden terminal problem.…”

A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

“…The lifetime of a WSN represents the total amount of time, over which the network remains operational and hence supports the application considered [11], [36]. Therefore, observe in Fig.…”

“…More explicitly, the attainable capacity of a wireless link strictly depends on the signal power, on the noise and on the interference levels at the receiver [9], [11]. Since the channel conditions are time-variant, maintaining the required BER can be a challenging task in a low-power WSN [36], as indicated in Fig. 3.…”

“…The NL in [8] was also defined as the instant, when the first data collection failure occurred. Note that there are various alternative NL definitions, which were discussed in [3], [8], [10], [13], [31], [34]- [36]. In Table I, we provide a comprehensive list of the NL definitions considered in the literature.…”

“…Additionally, application-specific QoS requirements, such as the latency, energy dissipation (ED), lifetime, bit error rate (BER), throughput, interference levels, time synchronization accuracy and data redundancy have to be taken into account during the deployment of the WSN in order to guarantee the seamless operation of the application considered [36], [51], as illustrated in Fig. 3.…”