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This paper considers the novel problem of upgrading a legacy network into a Software Defined Network (SDN) over multiple stages and saving energy in the upgraded network, or hybrid SDN. That is, in each stage, the problem at hand is to select and replace legacy switches with SDN switches and reroute traffic to power off as many unused cables as possible to save energy. Also, the operator must consider: (i) the available budget at each stage, (ii) maximum path delays, (iii) maximum link utilization, (iv) per-stage increase (decrease) in traffic size (upgrade cost), and (v) the Open Shortest Path First - Equal Cost Multi-Path protocol. This paper addresses two multi-path routing scenarios: 1) non-link-disjoint and 2) link-disjoint. It outlines a Mixed Integer Program and a heuristic algorithm for each scenario. The experimental results show that: (i) both solutions produce only up to 0.63% higher energy saving in scenario-1 than in scenario-2, (ii) the mixed integer program (heuristic algorithm) for both scenarios give an energy saving up to 71.93% (71.64%), (iii) using a larger budget and/or number of stages can increase the energy saving, and (iv) the saving achieved by the heuristic solution for each scenario is within 4% from the optimal saving.

This paper considers the problem of upgrading a legacy network into a Software Defined Network (SDN) over multiple stages and maximizing energy saving (ES) in the resulting upgraded network or hybrid SDN. In each stage, an operator needs to select and replace legacy switches with SDN switches and seek to switch off as many cables as possible over each link. This paper addresses the said problem where it considers (i) the available budget at each stage, (ii) maximum path delays, (iii) maximum link utilization, (iv) per-stage increase (decrease) in traffic size (upgrade cost), and (v) each non SDN switch must comply with the Open Shortest Path First (OSPF)-Equal Cost Multi-Path (ECMP) protocol. It outlines a Mixed Integer Program (MIP) and a heuristic algorithm called M-GMSU. The results show that (i) MIP and M-GMSU achieve ES of up to 71.93%, (ii) using a larger budget and/or number of stages increases ES, and (iii) the ES of M-GMSU is within 3.55% away from the optimal ES computed by MIP.

Software-defined networking (SDN) paradigm leads to innovation and simplicity in network configuration and management. Wireless sensor networks (WSNs) are becoming an essential component of the Internet of Things (IoTs). The combination of these two models gives rise to a new paradigm: Software-defined wireless sensor networks (SDWSNs). This paper provides a systematic review on SDWSNs. The study also discusses open challenges of further research on SDWSNs.

Wireless sensor networks with battery-operated nodes have the limitation in the energy availability at each node. One visible solution is to add the nodes with energy harvesting capability. By harnessing energy from the environment or other sources, the nodes can prolong its operation. This paper provides a brief review of energy harvesting systems - protocol, energy sources, and storage technologies. The study also discusses some opportunities offurther research on energy harvesting networks.