The efficient exploitation of renewable energy sources is crucial for addressing the global energy crisis and increase in CO2 emissions. Energy management system aggregators, functioning as nonprofit cooperatives within energy communities, manage renewable energy resources and incentivize residents towards self-consumption through dynamic, cost-attractive pricing schemes, receiving subsidies and long-term contracts as reward. This interaction between aggregator and residents is typically modeled using bi-level optimization frameworks, however, research studies often ignore the role of aggregators as self-consumption catalysts and the conflicting nature of the residents’ objectives. Moreover, lack of cooperation between the decision levels results in inflexible decision making when prioritizing between the respective objectives. This paper defines and formulates a bi-level multi-objective optimization problem for optimizing self-consumption in energy communities, while considering the residents’ welfare by maximizing satisfaction of their appliance-scheduling preferences and minimizing energy costs. We introduce the Bi-level Multi-Objective Energy Management System II (BiMO-EMS-II), composed of an Adaptive Population Transfer strategy, a Uniform Partially Mapped Crossover and a Decision Making heuristic with Cooperation. Our experimental evaluation has shown that BiMO-EMS-II simultaneously offers near-optimal self-consumption at the aggregator level and a high-quality trade-off between the conflicting objectives at the resident level, subject to different objective prioritization and decision-making assumptions.

The escalating global energy crisis and the increasing${\text{CO}_{2}}$emissions have necessitated the optimization of energy efficiency. The proliferation of Internet of Things (IoTs) devices, expected to reach 100 billion by 2030, contributed to this energy crisis and subsequently to the global${\text{CO}_{2}}$emissions increase. Concomitantly, climate and energy targets have paved the way for an escalating adoption of solar photovoltaic power generation in residences. The IoT integration into home energy management systems holds the potential to yield energy and peak demand savings. Optimizing device planning to mitigate${\text{CO}_{2}}$emissions poses significant challenges due to the complexity of user-defined preferences and consumption patterns. In this article, we propose an innovative IoT data platform, coinedSustainable Energy Management Framework (SEMF), which aims to balance the trade-off between the imported energy from the grid, users’ comfort, and${\text{CO}_{2}}$emissions.SEMFincorporates a Green Planning evolutionary algorithm, coinedGreenCap$^+$+, to facilitate load shifting of IoT-enabled devices, taking into consideration the integration of renewable energy sources, multiple constraints, peak-demand times, and dynamic pricing. Based on our experimental evaluation utilizing real-world data, our prototype system has outperformed the state-of-the-art approach by up to$\approx$29% reduction in imported energy,$\approx$35% increase in self-consumption of renewable energy, and$\approx$34% decrease in${\text{CO}_{2}}$emissions, while maintaining a high level of user comfort$\approx$94%-99%.

The European Green Deal has outlined ambitious near-term objectives to reduce CO2emissions and attain climate neutrality. Yet, effectively exploiting self-generated renewable energy within energy communities remains insufficiently explored. Energy management system aggregators often provide demand-response and ancillary services through demand-side management programs that incentivise residents using reduced energy costs or monetary compensation. However, their primary objectives of profit maximization and flexibility request satis-faction do not address the practical intricacies associated with energy self-consumption. This paper defines and formulates a bi-level multi-objective optimization problem for maximizing self-consumption within an energy community, while considering the residents' welfare by maximizing satisfaction of their appliance-scheduling preferences and minimizing energy costs. We propose a Bi-level Multi-Objective Energy Management System (BiMO-EMS) based on evolutionary computation for aggregator participation in the day-ahead energy market. Through experimental evaluation, we demonstrate that BiMO-EMS achieves a 98% approximation of the optimal solution with regards to self-consumption, while establishing a pricing scheme that offers a high-quality trade-off between the welfare-oriented objectives in terms of performance and diversity.

In this paper, we present an innovative framework whose objective is to allow drivers to recharge their Electric Vehicles (EVs) from the most environmentally friendly chargers using an intelligent hoarding approach. These chargers maximize renewable (e.g., solar) self-consumption, minimizing this way CO2 production and also the need for expensive stationary batteries on the electricity grid to store renewable energy that cannot be used otherwise. We model our problem as a Continuous k-Nearest Neighbor query, where the distance function is computed using Estimated Components (ECs), i.e., a query we term CkNN-EC. An EC defines a function that can have a fuzzy value based on some estimates. Specific ECs used in this work are: (i) the (available clean) power at the charger, which depends on the estimated weather; (ii) the charger availability, which depends on the estimated busy timetables that show when the charger is crowded; and (iii) the derouting cost, which is the time to reach the charger depending on estimated traffic. We devise the EcoCharge framework that combines these multiple non-conflicting objectives into an optimization task providing user-defined ranking means through an intuitive mobile GIS application. Particularly, our core algorithm uses lower and upper values derived from the ECs to recommend the top ranked EV chargers and present them through an intuitive map user interface to users. Our experimental evaluation with extensive synthetic and real traces from Germany, China, and USA along with EV charger data from Plugshare shows that EcoCharge meets the objective functions in an efficient manner, allowing continuous recomputation on the edge devices (e.g., Android Automotive OS, Android Auto or Apple Carplay).

In this demonstration paper, we present an innovative framework for sustainable Electric Vehicles (EVs) charging, dubbed EcoCharge, which utilizes an intelligent energy hoarding approach. Particularly, EcoCharge employs a Continuous k-Nearest Neighbor query, where the distance function is computed using Estimated Components (ECs) (i.e., a query we term CkNN-EC). An EC defines a function that can have a fuzzy value based on some estimates. Specific ECs used in this work are: (i) the (available clean) power at the charger, which depends on the estimated weather; (ii) the charger availability, which depends on the estimated busy timetables that show when the charger is crowded; and (iii) the derouting cost, which is the time to reach the charger depending on estimated traffic. Our framework combines these multiple non-conflicting objectives into an optimization task providing user-defined ranking means through an intuitive spatial application. The algorithm utilizes lower and upper interval values derived from ECs to recommend the top ranked EV chargers and present them through a map interface to users. We demonstrate EcoCharge using a complete prototype system developed using the Leaflet - OpenStreetMap library. In our demonstration scenario, attendees will have the opportunity to observe through mobile devices the benefits of EcoCharge by simulating its execution over various scheduled trips with real data retrieved from API requests (i.e., ECs).

Energy efficiency has become a primary optimization objective due to the global energy crisis and high levels of CO2emissions. Climate and energy targets have been leading to a growing utilization of solar photovoltaic power generation in residential buildings. As the number of IoT devices drastically increases, their automation through an intelligent home energy management system can provide energy and peak demand savings. The planning optimization of devices can be very challenging due to the unsophisticated user-defined preference rules. Existing solutions face convergence difficulties due to the management of multiple IoT devices tackling multiobjective problems. In this paper, we propose an innovative IoT data system, coined GreenCap, which utilizes a Green Planning evolutionary algorithm for load shifting of IoT-enabled devices, considering the integration of renewable energy sources, multiple constraints, peak-demand times, and dynamic pricing. We have implemented a complete prototype system available on Raspberry Pi and linked with openHAB framework. Our experimental evaluation with extensive real traces shows that the GreenCap prototype system efficiently generates a sustainable plan obtaining high levels of user comfort 92-99% along with ≈52% of self-consumption, while reducing ≈35% of the imported energy from the grid and ≈40% of CO2emissions.

In this demonstration paper, we present an innovative IoT data platform, coined GreenCap, which utilizes a Green Planning evolutionary algorithm for load shifting of IoT-enabled devices in smart environments that feature renewable energy sources. Particularly, GreenCap deploys a hybrid genetic algorithm with domain-specific local search heuristics, which results in a Memetic Algorithm (MA) that offers users an energy efficient allocation plan of their IoT devices, based on their personal preference rules (e.g., operate AC from 10am - 1pm). Our system allocates operations in the daily time-slots considering devices’ energy bounds to minimize the imported energy from the grid, exploit self-consumption and maximize users’ comfort. We demonstrate GreenCap using a complete prototype system available on Raspberry Pi, developed in Laravel using MariaDB and linked to openHAB framework. In our demonstration scenario, attendees will be able to observe through mobile devices the benefits of GreenCap by simulating its execution with real data for one week, using pre-configured or custom rules.

This demonstration paper introduces the Smart Out-of-Home Advertising Platform (SOAP), which leverages Geographic Information Systems (GIS) data and state-of-the-art Artificial Intelligence (AI) approaches to provide: (i) a documented, data-informed pricing model for billboards, which can be used to justify billboard prices to advertisers; and (ii) a set of non-dominated solutions (each corresponding to a different allocation of billboards to a given campaign) that explores the trade-offs between multiple conflicting objectives (e.g., cost and coverage). To the best of our knowledge, SOAP is the first to tackle such challenges in the context of Multi Objective Optimization (MOO).

This demo paper presents EnterCY, an integrated Virtual and Augmented Reality Tourism platform for Cyprus. The platform's web-based, spatio-temporal virtual exploration component allows potential visitors to explore the rich cultural heritage, variety of activities, and wealth of sightseeing locations in Cyprus before their visit. EnterCY also enhances tourists' experiences during their visit through its mobile component, which offers on-site visual and audio guidance, personalized recommendations, as well as entertaining and learning features (e.g., story-telling), based on mobile-friendly Augmented Reality, location-awareness and Machine Learning technologies. Through Immersive Reality technologies, the platform provides for an after visit experience by creating personalized 360 video mementos of tourists' tours and supports integrated features that allow for experience sharing in popular social media platforms.

The advancement of renewable energy infrastructure in smart buildings (e.g., photovoltaic) has highlighted the importance of energy self-consumption by energy-demanding IoT-enabled devices (e.g., heating/cooling, electromobility, and appliances), which refers to the process of intelligently consuming energy at the time it is available. This stabilizes the energy grid, minimizes energy dissipation on power lines but more importantly is good for the environment as energy from fossil sources with a high CO2 footprint is minimized. On the other hand, user comfort levels expressed in the form of Rule Automation Workflows (RAW) , are usually not aligned with renewable production patterns. In this work, we propose an innovative framework, coined IoT Meta-Control Firewall (IMCF + ) , which aims to bridge this gap and balance the trade-off between comfort, energy consumption, and CO2 emissions. The IMCF + framework incorporates an innovative Green Planner (GP) algorithm, which is an AI-inspired algorithm that schedules energy consumption with a variety of amortization strategies. We have implemented IMCF + and GP as part of a complete IoT ecosystem in openHAB and our extensive evaluation shows that we achieve a CO2 reduction of 45–59% to satisfy the comfort of a variety of user groups with only a moderate ≈ 3% in reducing their comfort levels.

Internet of Things (IoT) devices have penetrated massively into smart environments (e.g., smart-homes, smart-cars or more generally smart-anything). Besides data collection, many IoT devices also enable the execution of Rule Automation Workflows (RAW), which span from simple predicate statements to procedural workflows capturing a smart actuation pipeline. RAW aim to meet the convenience (comfort) level of users under specific conditions (e.g., raise room temperature to 22 C if cold), but unfortunately cannot express long-term objectives of users (e.g., consume less than 400 kWh in December). In this paper, we present an innovative system, coined IoT Meta-Control Firewall (IMCF), which internally deploys an AI-inspired Energy-Planner (EP) algorithm that exploits domain-specific operators to balance the trade-off between convenience and energy consumption in satisfying the RAW pipelines of users. IMCF filters the RAW pipelines in a way that these do not conflict with the long-term objectives of users (like a network firewall). Our experimental evaluation with extensive real traces from an apartment, a house, and campus dorms shows that IMCF achieves very high levels of user convenience while remaining within the target energy consumption budgets expressed by users.

The Internet of Things (IoT) revolution has introduced sensor-rich devices to an ever growing landscape of smart environments. A key component in the IoT scenarios of the future is the requirement to utilize a shared database that allows all participants to operate collaboratively, transparently, immutably, correctly and with performance guarantees. Blockchain databases have been proposed by the community to alleviate these challenges, however existing blockchain architectures suffer from performance issues. In this short paper we propose Triastore, a novel permissioned blockchain database system that carries out machine learning on the edge, abstracts machine learning models into primitive data blocks that are subsequently stored and retrieved from the blockchain. Triastore comprises of two internal routines, namely: (i) Proof of Federated Learning (PoFL), which trains in a distributed manner a global model for the ingested data; and (ii) Blockchain Consensus, which commits this generated model data on permissioned blockchain database. We present a detailed explanation of our data ingestion algorithm with relevant examples and carry out an experimental evaluation with image data from MNIST. The evaluation shows that our proposed data ingestion framework retains high levels of accuracy with low loss in data quality.

Internet-based Indoor Navigation Service-Oriented Architectures (IIN-SOA) organize signals collected by IoT-based devices to enable a wide range of novel applications indoors, where people spend 80--90% of their time. In this article, we study the problem of prefetching (or hoarding) the most important IoT data from an IIN-SOA to a mobile device, without knowing its user’s destination during navigation . Our proposed Grap (Graph Prefetching) framework structurally analyzes building topologies to identify important areas that become virtual targets to an online heuristic search algorithm we developed. We tested Grap with datasets from a real IIN-SOA and found it to be impressively accurate.

In this paper, we present a novel decaying operator for Telco Big Data (TBD), coined TBD-DP (Data Postdiction). Unlike data prediction, which aims to make a statement about the future value of some tuple, our formulated data postdiction term, aims to make a statement about the past value of some tuple, which does not exist anymore as it had to be deleted to free up disk space. TBD-DP relies on existing Machine Learning (ML) algorithms to abstract TBD into compact models that can be stored and queried when necessary. Our proposed TBD-DP operator has the following two conceptual phases: (i) in an offline phase, it utilizes a LSTM-based hierarchical ML algorithm to learn a tree of models (coined TBD-DP tree) over time and space; (ii) in an online phase, it uses the TBD-DP tree to recover data within a certain accuracy. In our experimental setup, we measure the efficiency of the proposed operator using a ~10GB anonymized real telco network trace and our experimental results in Tensorflow over HDFS are extremely encouraging as they show that TBD-DP saves an order of magnitude storage space while maintaining a high accuracy on the recovered data.

In this demonstration paper, we present the TBD-DP operator, which relies on existing Machine Learning (ML) algorithms to abstract Telco Big Data (TBD) into compact models that can be stored and queried when necessary. Our proposed TBD-DP operator has the following two conceptual phases: (i) in an offline phase, it utilizes a LSTM-based hierarchical ML algorithm to learn a tree of models (coined TBD-DP tree) over time and space; (ii) in an online phase, it uses the TBD-DP tree to recover data within a certain accuracy. Our framework also includes visual and declarative interfaces for a variety of telco-specific data exploration tasks. We demonstrate the efficiency of the proposed operator using SPATE, which is a novel TBD visual analytic architecture we have developed. Our demo will enable attendees to interactively explore synthetic antenna signal traces, we will provide, in both visual and SQL mode. In both cases, the performance of the propositions will be quantitatively conveyed to the attendees through dedicated dashboards.

Predominant smartphone OS localization subsystems currently rely on server-side localization processes, allowing the service provider to know the location of a user at all times. In this paper, we propose an innovative algorithm for protecting users from location tracking by the localization service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our proposed Temporal Vector Map (TVM) algorithm, allows a user to accurately localize by exploiting a k-Anonymity Bloom (kAB) filter and a bestNeighbors generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. We have evaluated our framework using a real prototype developed in Android and Hadoop HBase as well as realistic Wi-Fi traces scaling-up to several GBs. Our study reveals that TVM can offer fine-grained localization in approximately four orders of magnitude less energy and number of messages than competitive approaches.

Wi-Fi (or WLAN) based indoor navigation applications for mobiles rely on cloud-based services (s) that take care of a user's (u) localization task using structures called Radio Maps (RMs). It is imperative for u to have a stable WiFi connection in order to either continuously receive location updates from s or to download RMs a priori for offline navigation. Wi-Fi networks however, suffer from intermittent connectivity due to poor network planning that results in sparse deployment of access points and effectively areas where Wi-Fi coverage cannot be guaranteed. This inherently affects the localization accuracy and therefore the navigation experience of users. In this paper, we propose an innovative framework for accurate and fast indoor localization over an intermittently connected WiFi network, coined Prefetching Localization (PreLoc). In Preloc, we prioritize the download of RM records based on knowledge acquired from historic traces of other users inside the same building. Instead of downloading the complete RM from s to u, we propose a Probabilistic Group Selection (PGS) strategy, which identifies RM records that have a higher probability of being necessary to a user moving inside a target area. We have evaluated our framework using a real prototype developed in Android, as well as realistic Wi-Fi traces we collected at the University of Cyprus. Our experimental study reveals that PreLoc using PGS and conventional fingerprint-based indoor positioning algorithms can yield accuracy that is as good as using the same algorithms with a complete RM, even under scenarios of weak Wi-Fi coverage.

In this paper we present a comprehensive architecture to carry out experimental repeatability studies on clusters of smartphones. Our architecture is founded on Smart Lab, our in-house architecture for managing real and virtual smartphones via an intuitive Web user interface. Our presented architecture consists of several exciting components for re-programming and instrumenting smartphones to perform application testing and data gathering in a facile manner, as well as executing mockup experiments by "feeding" the devices with GPS/sensor readings. We will particularly demonstrate the various components of our architecture that encompasses smartphone sensor data collected by mobile users and organized in our distributed NoSQL document store. The given datasets can then be replayed on our test bed comprising of real and virtual smartphones accessible to developers through our Web 2.0 user interface. We present the applicability of our architecture through various mockup experiments over different application scenarios.

Indoor Positioning Systems (IPS) have recently received considerable attention, mainly because GPS is unavailable in indoor spaces and consumes considerable energy. On the other hand, predominant Smartphone OS localization subsystems currently rely on server-side localization processes, allowing the service provider to know the location of a user at all times. In this paper, we propose an innovative algorithm for protecting users from location tracking by the localization service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our proposed Temporal Vector Map (TVM) algorithm, allows a user to accurately localize by exploiting a k-Anonymity Bloom (kAB) filter and a bestNeighbors generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. We have evaluated our framework using a real prototype developed in Android and Hadoop HBase as well as realistic Wi-Fi traces scaling-up to several GBs. Our analytical evaluation and experimental study reveal that TVM is not vulnerable to attacks that traditionally compromise k-anonymity protection and indicate that TVM can offer fine-grained localization in approximately four orders of magnitude less energy and number of messages than competitive approaches.

In this demonstration paper, we present a novel framework for searching objects (e.g., images, videos, etc.) captured by the users in a mobile social community. Our framework, is founded on an in-situ data storage model, where captured objects remain local on their owners smartphones and searches then take place over a novel lookup structure we compute dynamically. Initially, a query user invokes a search to find an object of interest. Our structure concurrently optimizes several conflicting objectives (i.e., it minimizes energy consumption, minimizes search delay and maximizes query recall), using a Multi-Objective Optimization approach and calculates a diverse set of high quality non-dominated Query Routing Trees (QRTs), in a single run. The optimal set is then forwarded to the query user (decision maker) to select a particular QRT to be searched based on instant requirements and preferences. To demonstrate the capabilities of SmartP2P during the conference, we will utilize our cloud of smartphone devices, i.e. the SmartLab testbed composed of 40+ Android smartphones and tablets, as well as mobility and social patterns derived by Microsofts Geolife project, DBLP and Pics n Trails. We will allow the attendees to use a real SmartLab Android device to query our local Smartphone Network using any of the four algorithmic choices provided by the SmartP2P framework. The query device will then be provided with the optimal QRTs and the attendees will be able to visually decide the optimal QRT to be searched. A P2P search on the Smartphone Network will follow making available to the query user the desired objects of interest, in an optimal manner. The conference attendees will be able to appreciate how social content can be efficiently shared with other attendees within close proximity without revealing their personal content to a centralized authority.

In this demonstration we present SmartLab1, an exciting experimental testbed of approximately 40+ real Android Smartphones, plus emulated devices, deployed at the Department of Computer Science building at the University of Cyprus. SmartLab provides a public, permanent testbed for the development and testing of smartphone network applications via an intuitive web-based interface. Registered users can upload and install Android executables (APKs) on a number of Android smartphones, capture their output, reboot the devices, create concurrent interactive jobs using MonkeyRunner scripts, interact with the remote devices and many other exciting features. SmartLab aims to facilitate research in smartphone network programming environments, communication protocols, system design, and applications.

The bulk of social network applications for smart phones (e.g., Twitter, Face book, Foursquare, etc.) currently rely on centralized or cloud-like architectures in order to carry out their data sharing and searching tasks. Unfortunately, the given model introduces both data-disclosure concerns (e.g., disclosing all captured media to a central entity) and performance concerns (e.g., consuming precious smart phone battery and bandwidth during content uploads). In this paper, we present a novel framework, coined Smart Opt, for searching objects (e.g., images, videos, etc.) captured by the users in a mobile social community. Our framework, is founded on an in-situ data storage model, where captured objects remain local on their owner's smart phones and searches then take place over a novel lookup structure we compute dynamically, coined the Multi-Objective Query Routing Tree (MO-QRT). Our structure concurrently optimizes several conflicting objectives (i.e., it minimizes energy consumption, minimizes search delay and maximizes query recall), using a Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) that calculates a diverse set of high quality non-dominated solutions in a single run. We assess our ideas with mobility patterns derived by Microsoft's Geolife project and social patterns derived by DBLP. Our study reveals that Smart Opt can yield query recall rates of 95%, with one order of magnitude less time and two orders of magnitude less energy than its competitors.

Mobile agents are often used in wireless sensor networks for distributed target detection with the goal of minimizing the transmission of non-critical data that negatively affects the performance of the network. A challenge is to find optimal mobile agent routes for minimizing the data path loss and the sensors energy consumption as well as maximizing the data accuracy. Existing approaches deal with the objectives individually, or by optimizing one and constraining the others or by combining them into a single objective. This often results in missing “good” tradeoff solutions. Only few approaches have tackled the Mobile Agent-based Distributed Sensor Network Routing problem as a Multiobjective Optimization Problem (MOP) using conventional Multi-Objective Evolutionary Algorithms (MOEAs). It is well known that the incorporation of problem specific knowledge in MOEAs is a difficult task. In this paper, we propose a problem-specific MOEA based on Decomposition (MOEA/D) for optimizing the three objectives. Experimental studies have shown that the proposed problem-specific approach performs better than two conventional MOEAs in several WSN test instances.

In this paper, we propose a Subproblem-dependent Heuristic (SH) for MOEA/D to deal with the Deployment and Power Assignment Problem (DPAP) in Wireless Sensor Networks (WSNs). The goal of the DPAP is to assign locations and transmit power levels to sensor nodes for maximizing the network coverage and lifetime objectives. In our method, the DPAP is decomposed into a number of scalar subproblems. The subproblems are optimized in parallel, by using neighborhood information and problem-specific knowledge. The proposed SH probabilistically alternates between two DPAP-specific strategies based on the subproblems objective preferences. Simulation results have shown that MOEA/D performs better than NSGA-II in several WSN instances.

The K-connected deployment and power assignment problem (DPAP) in WSNs aims at deciding both the sensor locations and transmit power levels, for maximizing both the network coverage and lifetime under K-connectivity constraints, in a single run. It is shown that the multi-objective evolutionary algorithm based on decomposition (MOEA/D) is a strong enough tool for dealing with unconstraint real life problems (such as DPAP), emphasizing the importance of incorporating problem specific knowledge for increasing its efficiency. Since the K-connected DPAP requires constraint handling, several techniques are investigated and compared, including a DPAP-specific repair heuristic (RH) that transforms an infeasible network design into a feasible one and maintains the MOEA/D's efficiency simultaneously. This is achieved by alternating between two repair strategies, which favor one objective each. Simulation results have shown that the MOEA/D-RH performs better than the popular constrained NSGA-II in several network instances.

Wireless sensor networks deployment and power assignment problems (DPAPs) for maximizing the network coverage and lifetime respectively, have received increasing attention recently. Classical approaches optimize these two objectives individually, or by combining them together in a single objective, or by constraining one and optimizing the other. In this paper, the two problems are formulated as a multi-objective DPAP and tackled simultaneously. Problem-specific encoding representation and genetic operators are designed for the DPAP and a multi-objective evolutionary algorithm based on decomposition (MOEA/D) is specialized. The multi-objective DPAP is decomposed into many scalar subproblems which are solved simultaneously by using neighborhood information and network knowledge. Simulation results have shown the effectiveness of the proposed evolutionary components by providing a high quality set of alternative solutions without any prior knowledge on the objectives preference, and the superiority of our problem-specific MOEA/D approach against a state of the art MOEA.

Wireless sensor networks design requires high quality location assignment and energy efficient power assignment for maximizing the network coverage and lifetime. Classical deployment and power assignment approaches optimize these two objectives individually or by combining them together in a single objective or by constraining one and optimizing the other. In this article a multi-objective deployment and power assignment problem (DPAP) is formulated and a multi-objective evolutionary algorithm based on decomposition (MOEA/D) is specialized. Following the MOEA/D's framework the above multiobjective optimization problem (MOP) is decomposed into many scalar single objective problems. The sub-problems are solved simultaneously by using neighborhood information. Additionally, unique problem-specific, parameter-rising, genetic operators and local search heuristics were designed specifically for the DPAP. In addition, a new encoding scheme is designed to represent a WSN based on the DPAP's design variables. Simulation results show that MOEA/D provides a high quality set of alternative solutions without any prior knowledge on the objectives preference.

Cost-effective topology control is critical in wireless sensor networks. While much research has been carried out in this aspect using various methods, no attention has been made on utilizing modern heuristics for this purpose. This paper proposes a memetic algorithm-based solution for energy-aware topology control for wireless sensor networks. This algorithm (called ToCMA), using a combination of problem-specific light-weighted local search and genetic algorithm, is able to solve the minimum energy network connectivity (MENC) this NP-hard problem in an approximated manner that performs better than the classical minimum spanning tree (MST) solution. The outcomes of ToCMA can also be utilized for various network optimization and fault-tolerant purposes.