LEACH-like clustering protocols focus mainly on the low-power, low-rate, and low-wakeup network applications, and work in a multi-round clustering strategy that causes frequent handovers of cluster heads, thus less support for real-time services which require stable cluster topologies. Besides, these protocols are faced with respective drawbacks such as sub-optimality of selected heads, costly node-BS (base station) energy overheads, lack of runtime cluster maintenance, etc. This paper proposes RANCE, a randomly centralized and on-demand clustering protocol, aiming at prolonging nodes’ clustered time to support inter-nodes collaboration while being energy efficient in mobile ad hoc networks. First, RANCE designs a randomly centralized cluster head selection mechanism in which every node in the local wireless network is eligible to initiate the centralized cluster head selection, so that the self-organizing characteristics of mobile ad hoc nodes can be utilized for head selection optimization. Second, taking into account the wireless volatility caused by changes of topology, obstacles, signal strength, etc., the fine-grained cluster relationships maintenance is provided by means of multi-level aliveness and adaptive bidirectional heartbeat packets. Third, RANCE works in an event-driven and on-demand manner instead of a time-triggered manner in LEACH-like protocols, to reduce the impact on continuous services caused by frequent cluster head handovers among all nodes. Simulation results show that RANCE provides longer clustered time (over 99% of nodes’ lifetime in networks more than 100 nodes) and good clustering scalability with high consistency at minimum energy cost, and exhibits good potentials in mobile wireless environments that are infrastructure-less/poor for continuous missions.

The construction and running of hydropower plants does not merely involve hydropower generation itself but also facilities surveillance, water quality monitoring, harmful creatures tracking, etc., thus requiring mobile and collaborative monitoring capabilities. MANET (Mobile Ad Hoc Networks), with its mobility, flexibility, and robustness in volatile networking environment, is a competitive candidate to fulfill such tasks. Nevertheless its distributed structure prevents the effective collaboration between MANET nodes. SDN (Software-Defined Networking) provides the centralized control over the network underlay. This paper proposes the SDN-controlled IEEE 802.11 MANET swarm for mobile monitoring in hydropower plants. First, the MANET node is implemented by integrating Raspberry Pi with cameras, various sensors, etc., into the low-cost wheeled mobile hardware to enable sensibility and mobility. Then, multiple such MANET nodes are networked through ad hoc protocols, to construct a flexible and distributed MANET underlay; meanwhile, to implement centralized control over the MANET underlay, every MANET node is equipped with OpenFlow switch software (e.g., Open vSwitch) so that OpenFlow directives issued by the SDN controller can be understood and executed, hence the SDN overlay on top of the MANET underlay. Finally, OpenFlow is extended to offer physical actions such as mobility and sensibility manipulation, beyond pure data forwarding in traditional SDN applications, to achieve mobile monitoring in hydropower plants. The SDN-controlled MANET swarm features mobile and sensing capabilities, flexible networking through ad hoc protocols, and efficient and unified control by SDN. Experiment results prove the feasibility of this network architecture.

Geo-distributed machine learning (Geo-DML) adopts a hierarchical training architecture that includes local model synchronization within the data center and global model synchronization (GMS) across data centers. However, the scarce and heterogeneous wide area network (WAN) bandwidth can become the bottleneck of training performance. An intelligent optical device (i.e., reconfigurable optical all-drop multiplexer) makes the modern WAN topology reconfigurable, which has been ignored by most approaches to speed up Geo-DML training. Therefore, in this paper, we study scheduling algorithms to accelerate model synchronization for Geo-DML training with consideration of the reconfigurable optical WAN topology. Specifically, we use an aggregation tree for each Geo-DML training job, which helps to reduce model synchronization communication overhead across the WAN, and propose two efficient algorithms to accelerate GMS for Geo-DML: MOptree, a model-based algorithm for single job scheduling, and MMOptree for multiple job scheduling, aiming to reconfigure the WAN topology and trees by reassigning wavelengths on each fiber. Based on the current WAN topology and job information, mathematical models are built to guide the topology reconstruction, wavelength, and bandwidth allocation for each edge of the trees. The simulation results show that MOptree completes the GMS stage up to 56.16% on average faster than the traditional tree without optical-layer reconfiguration, and MMOptree achieves up to 54.6% less weighted GMS time.

Metaheuristic algorithm is a generalization of heuristic algorithm that can be applied to almost all optimization problems. For optimization problems, metaheuristic algorithm is one of the methods to find its optimal solution or approximate solution under limited conditions. Most of the existing metaheuristic algorithms are designed for serial systems. Meanwhile, existing algorithms still have a lot of room for improvement in convergence speed, robustness, and performance. To address these issues, this paper proposes an easily parallelizable metaheuristic optimization algorithm called team competition and cooperation optimization (TCCO) inspired by the process of human team cooperation and competition. The proposed algorithm attempts to mathematically model human team cooperation and competition to promote the optimization process and find an approximate solution as close as possible to the optimal solution under limited conditions. In order to evaluate the performance of the proposed algorithm, this paper compares the solution accuracy and convergence speed of the TCCO algorithm with the Grasshopper Optimization Algorithm (GOA), Seagull Optimization Algorithm (SOA), Whale Optimization Algorithm (WOA) and Sparrow Search Algorithm (SSA). Experiment results of 30 test functions commonly used in the optimization field indicate that, compared with these current advanced metaheuristic algorithms, TCCO has strong competitiveness in both solution accuracy and convergence speed.