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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.

The Internet of Things (IoT) revolution has massively 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 vision paper we propose Triabase, 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. As such, it does not store detailed records on a medium, like blockchains, which is fundamentally very slow due to the expensive verification process. We lay out the primitive architectural blocks of our design, the requirements and the inherent challenges. Triabase employs technical novelties in respect to its consensus protocol, namely the notion of Proof-of-Federated-Learning (PoFL). The Triabase prototype system is implemented in the Hyperledger Fabric blockchain framework, upon which encouraging preliminary findings have been drawn.

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.

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.

This paper discusses vocational training within the context of SMEs and presents the approach of the ELEVATE project which aims to counter the issues encountered by previous efforts as revealed in the study of related work on effective vocational training and e-Training strategies. Moreover, the ELEVATE vocational training approach is presented and rationalized based on specific principles for the effective design of vocational e-Training. The paper concludes by presenting the blended learning and competence-based training aspects of the ELEVATE pedagogic methodology.

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. Recently, 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.

Computer technology seems to be very useful to support collaborative learning scenarios and thus Computer Supported Collaborative Learning. The main goal of this paper is to compare two of the most promising Collaborative Virtual Environment platforms, namely Croquet and Second Life, in terms of their potential to support collaborative e-learning scenarios among geographically separated users. Both platforms are examined by validating the platforms’ features, philosophy and policies against some basic design principles for collaborative virtual learning environments in order to better assess their design adequacy for online learning. Finally, this paper presents the user evaluation results, which was conducted by undergraduate students in order to assess the potential of both platforms for supporting collaborative e-learning scenarios.

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.

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.

In this paper we present a pedagogically-documented approach and model that facilitates the specialised and personalised e-training of professionals and end-users in application software products. The main focus of our methodology is on blended learning, authentic learning and competence based learning. Via blended learning, face to face as well as online teaching is used. Moreover, there is an e-training solution that can be used either as stand alone solution, for teaching a particular competence or can be used as a support to face to face teaching (that is revisit a competence taught in face to face). The e-Training component of the ELEVATE environment uses authentic learning by utilising “live” software systems. Competence based learning is used to identify the competence gap as well as tailor the training that the individual learner needs to attain the competence required.

This paper reviews and compares the most promising collaborative virtual environment platforms, which have been used or proposed for supporting educational activities in terms of their potential to support collaborative e-learning. The most promising environment according to the results of this review is Second Life. Second Life is further examined by validating the platform's features, philosophy and policies against some basic design principles for collaborative virtual learning environments in order to better assess its design adequacy for online learning. Furthermore, this paper presents the features that we have implemented within the Second Life platform, in order to facilitate the jigsaw collaborative e-learning scenario. Finally, we present a case study concerning the evaluation of Second Life by undergraduate students in order to assess its potential to support collaborative e-learning techniques.

For computational intelligence to be useful in creating game agent AI, we need to focus on creating interesting and believable agents rather than just learn to play the games well. To this end, we propose a way to use multiobjective evolutionary algorithms to automatically create populations of non-player characters (NPCs), such as opponents and collaborators, that are interestingly diverse in behaviour space. Experiments are presented where a number of partially conflicting objectives are defined for racing game competitors, and multiobjective evolution of Genetic Programming-based controllers yield pareto fronts of interesting controllers.

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.