text: We present our Zero Energy Network'' (ZEN) protocol stack for energy harvesting wireless sensor networks applications. The novelty in our work is @math fold: (1) Energy harvesting aware fully featured MAC layer. Carrier sensing, Backoff algorithms, ARQ, RTS CTS mechanisms, Adaptive Duty Cycling are either auto configurable or available as tunable parameters to match the available energy (b) Energy harvesting aware Routing Protocol. The multi-hop network establishes routes to the base station using a modified version of AODVjr routing protocol assisted by energy predictions. (c) Application of a time series called Holt-Winters'' for predicting the incoming energy. (d) A distributed smart application running over the ZEN stack which utilizes a multi parameter optimized perturbation technique to optimally use the available energy. The application is capable of programming the ZEN stack in an energy efficient manner. The energy harvested distributed smart application runs on a realistic solar energy trace with a three year seasonality database. We implement a smart application, capable of modifying itself to suit its own as well as the network's energy level. Our analytical results show a close match with the measurements conducted over EHWSN testbed.
  @cite_21: Multi-hop random access networks have received much attention because of their distributed nature which facilitates deploying many new applications over the sensor and computer networks. Recently, utility maximisation framework is applied in order to optimise performance of such networks; however, proposed algorithms result in large transmission delays. In this study, the authors will analyse delay in random access multi-hop networks and solve the delay-constrained utility maximisation problem. We define the network utility as a combination of rate utility and energy cost functions and solve the following two problems: ‘optimal medium access control with link delay constraint’ and, ‘optimal congestion and contention control with end-to-end delay constraint’. The optimal tradeoff between delay, rate and energy is achieved for different values of delay constraint and the scaling factors between rate and energy. Eventually, linear and super-linear distributed optimisation solutions are proposed for each problem and their performance are compared in terms of convergence and complexity.
  @cite_23: Recently, there has been a substantial interest in the design of systems that receive their energy from regenerative sources such as solar cells. In contrast to approaches that minimize the power consumption subject to performance constraints, we are concerned with optimizing the performance of an application while respecting the limited and time-varying amount of available power. In this paper, we address power management of, e.g., wireless sensor nodes which receive their energy from solar cells. Based on a prediction of the future available energy, we adapt parameters of the application in order to maximize the utility in a long-term perspective. The paper presents a formal model of the corresponding optimization problem including constraints concerning buffer sizes, timing, and rates. Instead of solving the optimization problem online which may be prohibitively complex in terms of running time and energy consumption, we apply multiparametric programming to precompute the application parameters offline for different environmental conditions and system states. In order to guarantee sustainable operation, we propose a hierarchical software design which comprises a worst-case prediction of the incoming energy. As a further contribution, we suggest a new method for approximate multiparametric linear programming which substantially lowers the computational demand and memory requirement of the embedded software. Our approaches are evaluated using long-term measurements of solar energy in an outdoor environment.
  @cite_7: A systematic understanding of the decomposability structures in network utility maximization is key to both resource allocation and functionality allocation. It helps us obtain the most appropriate distributed algorithm for a given network resource allocation problem, and quantifies the comparison across architectural alternatives of modularized network design. Decomposition theory naturally provides the mathematical language to build an analytic foundation for the design of modularized and distributed control of networks. In this tutorial paper, we first review the basics of convexity, Lagrange duality, distributed subgradient method, Jacobi and Gauss-Seidel iterations, and implication of different time scales of variable updates. Then, we introduce primal, dual, indirect, partial, and hierarchical decompositions, focusing on network utility maximization problem formulations and the meanings of primal and dual decompositions in terms of network architectures. Finally, we present recent examples on: systematic search for alternative decompositions; decoupling techniques for coupled objective functions; and decoupling techniques for coupled constraint sets that are not readily decomposable
  @cite_11: 
summary: Several optimization techniques have been discussed in literature for battery driven multihop WSN's in the past where the objective is to maximize the network lifetime by minimizing energy consumption @cite_21 , @cite_7 . With a view on utilizing the harvested energy in an optimized manner, adapting the performance of an application while respecting the limited and time-varying amount of available power in EHWSN is discussed in @cite_23 . A formal model that actively adapts application parameters such as rate of sensing'' is used to optimize the performance. The simulation study does not consider network related parameters or network wide energy levels. A Lazy Scheduling Algorithm'' is proposed and tested for its effectiveness by assigning several power values to the system in @cite_11 . Task admittance and future energy prediction is carried out with energy variability characterization curves generated by modeling the power source.