Decentralized Collective Learning for Self-managed Sharing Economies

The Internet of Things equips citizens with a phenomenal new means for online participation in sharing economies. When agents self-determine options from which they choose, for instance, their resource consumption and production, while these choices have a collective systemwide impact, optimal decision-making turns into a combinatorial optimization problem known as NP-hard. In such challenging computational problems, centrally managed (deep) learning systems often require personal data with implications on privacy and citizens’ autonomy. This article envisions an alternative unsupervised and decentralized collective learning approach that preserves privacy, autonomy, and participation of multi-agent systems self-organized into a hierarchical tree structure. Remote interactions orchestrate a highly efficient process for decentralized collective learning. This disruptive concept is realized by I-EPOS, the Iterative Economic Planning and Optimized Selections, accompanied by a paradigmatic software artifact. Strikingly, I-EPOS outperforms related algorithms that involve non-local brute-force operations or exchange full information. This article contributes new experimental findings about the influence of network topology and planning on learning efficiency as well as findings on techno-socio-economic tradeoffs and global optimality. Experimental evaluation with real-world data from energy and bike sharing pilots demonstrates the grand potential of collective learning to design ethically and socially responsible participatory sharing economies

A combinatorial optimisation approach to non-market environmental benefit aggregation

This paper considers the use of spatial microsimulation in the aggregation of regional environmental benefit values. The developed spatial microsimulation model uses simulated annealing to match the Irish Census of Agriculture data to a Contingent Valuation Survey that contains information on Irish farmers’ willingness to pay (WTP) to have the corncrake restored as a common sight in the Irish countryside. We then use this matched farm survey and Census information to produce regional and national total WTP figures, and compare these to figures derived using more standard approaches to calculating aggregate environment benefit values. The main advantage of the spatial microsimulation approach for environmental benefit value aggregation is that it allows one to account for the heterogeneity in the target population. Results indicate that the microsimulation modelling approach provides aggregate WTP estimates of a similar magnitude as those produced using the usual sample mean WTP aggregation at the national level, but yields regional aggregate values which are significantly different

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Cooperative relay selection for load balancing with mobility in hierarchical WSNs: A multi-armed bandit approach

© 2013 IEEE. Energy efficiency is the major concern in hierarchical wireless sensor networks(WSNs), where the major energy consumption originates from radios for communication. Due to notable energy expenditure of long-range transmission for cluster members and data aggregation for Cluster Head (CH), saving and balancing energy consumption is a tricky challenge in WSNs. In this paper, we design a CH selection mechanism with a mobile sink (MS) while proposing relay selection algorithms with multi-user multi-armed bandit (UM-MAB) to solve the problem of energy efficiency. According to the definition of node density and residual energy, we propose a conception referred to as a Virtual Head (VH) for MS to collect data in terms of energy efficiency. Moreover, we naturally change the relay selection problem into permutation problem through employing the two-hop transmission in cooperative power line communication, which deals with long-distance transmission. As far as the relay selection problem is concerned, we propose the machine learning algorithm, namely MU-MAB, to solve it through the reward associated with an increment for energy consumption. Furthermore, we employ the stable matching theory based on marginal utility for the allocation of the final one-to-one optimal combinations to achieve energy efficiency. In order to evaluate MU-MAB, the regret is taken advantage to demonstrate the performance by using upper confidence bound (UCB) index. In the end, simulation results illustrate the efficacy and effectiveness of our proposed solutions for saving and balancing energy consumption

Simulated evolution for timing and low power VLSI standard cell placement

Abstract This paper presents a Fuzzy Simulated Evolution algorithm for VLSI standard cell placement with the objective of minimizing power, delay and area. For this hard multiobjective combinatorial optimization problem, no known exact and efficient algorithms exist that guarantee finding a solution of specific or desirable quality. Approximation iterative heuristics such as Simulated Evolution are best suited to perform an intelligent search of the solution space. Due to the imprecise nature of design information at the placement stage the various objectives and constraints are expressed in the fuzzy domain. The search is made to evolve toward a vector of fuzzy goals. Variants of the algorithm which include adaptive bias and biasless simulated evolution are proposed and experimental results are presented. Comparison with genetic algorithm is discussed. r 2003 Elsevier Ltd. All rights reserved

Evidential reasoning-based airline network design for long-haul transportation in express delivery

Kod hitne isporuke, za ekspresni prijevoz robe uvelike se koristi avionska dostava zbog visoke učinkovitosti i sigurnosti, najvažnijih za kupce. Velika količina tereta prisiljava ekspresne kompanije na uspostavu vlastitih avionskih mreža u svrhu poboljšanja efikasnosti isporuke i snižavanja troškova prijevoza. Uobičajena optimalna rješenja imaju vrlo složeno dizajnirane mreže s čvorištima za prijelaz; štoviše, uvjeti, uključujući količinu oborina, volumen željezničkog prijevoza, prosječnu propusnost i infrastrukturu hardwera, ne mogu se u potpunosti uzeti u obzir. U ovom radu formuliramo problem dizajna ekspresne avionske mreže kao proces vrednovanja zasnovan na evidentnom zaključivanju s multidimenzijskim podacima. U svrhu osiguranja fleksibilne scheme za rješavanje zadatka procjenjivanja primijenjena je Dempster-Shafer teorija dokaza, a postupak evidentnog zaključivanja specifičan za dizajniranje ekspresnih mreža predlaže se kako bi se osigurao najbolji izbor čvorišta za prijelaz. Predloženi pristup je primijenjen kod projektiranja avionske mreže za ShunFeng ekspres kompaniju, a analiza tog slučaja pokazuje da se predloženom schemom može osigurati razumna transportna mreža uz više učinkovitosti i niže cijene.In express delivery, air couriers have been used extensively for transporting express freight due to its high efficiency and security, which are the most important considerations of the customers. The large volume of cargo forces the express company to build its own airline networks, aiming at improving the efficiency of delivery and reducing the transport cost. The conventional optimal solutions are very complex for use in designing networks with transferring hubs, and, in addition, the port conditions, including the volume of rainfall, the volume of railway transportation, average throughput, and hardware infrastructure, cannot be considered fully. In this paper, we formulate the express airline network design problem as an evidential reasoning-based evaluation process with multi-dimensional data. We used the Dempster-Shafer evidence theory to provide a flexible scheme to solve the evaluation task, and we proposed an evidential reasoning process specific for designing an express network to determine the best choice of the transferring hub. The proposed approach was applied in the design of the airline network for the ShunFeng express company, and the case study demonstrated that the proposed scheme can obtain a reasonable transport network with higher efficiency and lower cost