This paper addresses distributed task allocation in complex scenarios modeled using the distributed constraint optimization problem (DCOP) formalism. It is well known that DCOP, when used to model complex scenarios, generates problems with exponentially growing number of parameters. However, those scenarios are becoming ubiquitous in real-world applications. Therefore, approximate solutions are necessary. We propose and evaluate an algorithm for distributed task allocation. This algorithm, called Swarm-GAP, is based on theoretical models of division of labor in social insect colonies. It uses a probabilistic decision model. Swarm-GAP is experimented both in a scenario from RoboCup Rescue and an abstract simulation environment. We show that Swarm-GAP achieves similar results as other recent proposed algorithm with a reduction in communication and computation. Thus, our approach is highly scalable regarding both the number of agents and tasks.

Using Swarm-GAP for Distributed Task Allocation in Complex Scenarios.

Fleet management: A vehicle and driver assignment model

Activities of daily living (ADL) or activities of daily working (ADW) may be affected by changes in a person’s health or well-being. Measuring progressive changes in one activity or multiple activities is representative of behavioural variations. By inspecting the trends in multiple activities, it is possible to identify and predict human behavioural changes. We refer to the trends in people’s behaviour as behavioural evolution. In this paper, we propose a novel indicator to measure the progressive changes representing a participant’s behavioural evolution. The proposed indicator presents activities as a holistic measure, which first combine multi-activities and then measure the progressive changes in the combined activities for each single day. Real data sets were collected from a wireless sensor network and used to examine our proposed technique. As part of this process, we were able to quantify progressive changes for individual and aggregated activities. Our experimental results demonstrated that: (1) the proposed approach can identify and distinguish normal and abnormal behaviours; (2) large data sets gathered from sensors in an intelligent environment represented in various time series can be visualised in a simple and more understandable format; (3) identifying trends in ADLs or ADWs is a relevant means of sharing information with carers or supervisors.

The human behaviour indicator: A measure of behavioural evolution

Tracking objects in video surveillance is a challenging task for public security. The advent of the semantic approach has greatly raised the growth of anomaly detection. However, the current anomaly detection methods typically experience problems like inadequate use of movement patterns and inconsistency on various datasets. This research proposed a system to enhance the efficiency of anomaly detection in video surveillance. The proposed system consists of two parts that involves object tracking and suspicious activity detection. The overall framework detects and tracks the abnormal objects in video surveillance. The transfer learning-based ResNet tracking has been used for object tracking. Distance Metric Learning (DML) method has been used for detecting suspicious activities in video surveillance. The results are estimated to analyze the efficiency of the proposed method. The proposed network classifier is compared with the existing ResNet and VGG-16 network. The proposed method provides 99% accuracy that had high performance compared to other existing methods.

Suspicious Activity Detection Using Transfer Learning Based ResNet Tracking from Surveillance Videos

While network path generation has been one of the representative Non-deterministic Polynomial-time (NP)-hard problems, changes of network topology invalidate the effectiveness of the existing metaheuristic algorithms. This research proposes a new and efficient path generation framework that considers dynamic topology changes in a complex network. In order to overcome this issue, Multi-directional and Parallel Ant Colony Optimization (MPACO) is proposed. Ant agents are divided into several groups and start at different positions in parallel. Then, Gaussian Process Regression (GPR)-based pheromone update method makes the algorithm more efficient. While the proposed MPACO algorithm is more efficient than the existing ACO algorithm, it is limited in a network with topological changes. In order to overcome the issue, the MPACO algorithm is modified to the Convolution MPACO (CMPACO) algorithm. The proposed algorithm uses the pheromone convolution method using a discrete Gaussian distribution. The proposed pheromone updating method enables the generation of a more efficient network path with comparatively less influence from topological network changes. In order to show the effectiveness of CMPACO, numerical networks considering static and dynamic conditions are tested and compared. The proposed CMPACO algorithm is considered a new and efficient parallel metaheuristic method to consider a complex network with topological changes.

Development of a Convolution-Based Multi-Directional and Parallel Ant Colony Algorithm Considering a Network with Dynamic Topology Changes

Distributed generalized assignment problem (D-GAP) is very popular in scalable multi-agent systems. However, existing algorithms are either not effective or efficient in large-scale or highly dynamic domains owing to limited communications and computational resources. In this paper, we present a novel approach named intelligent routing algorithm (IRA) to address this issue. In IRA, in order to reduce communication load, a decentralized model for agents is proposed to jointly search for optimized solutions. Moreover, due to the complexity of distributed generalized assignment problem (D-GAP) in a massive multi-agent system where agents cannot perform optimal search based on their local views, we propose a heuristic algorithm that can find an approximate optimized solution. By inferring knowledge from their previous communicated searches, agents are able to predict how to deploy future similar searches more efficiently. If an agent can solve some parts of D-GAP well, similar searches will be sent to it. By taking advantage of the accumulation effect to agents’ local knowledge, agents can independently make simple decisions with highly reliable performance and limited communication overheads. The simulation and the experimental results demonstrate the feasibility and efficiency of our algorithm.

Heuristic routing algorithm toward scalable distributed generalized assignment problem

Mobile activity recognition is an effective approach to understanding human context in real time Existing methods based on supervised learning that require a large amount of training samples for building activity recognition models The collection of labeled training samples is a boring process and most users are reluctant to get involved Crowdsourcing is a simple and potential approach to collecting the training samples and building accurate activity recognizers Since different people usually have different physical features and behavior patterns, an accurate activity recognition model cannot be constructed directly from the training samples collected by crowdsourcing In this paper, we have proposed a Mixture Expert Model for Activity Recognition (MEMAR) based on feedback and crowdsourcing samples The proposed model can continuously discover the difference between user activity and crowdsourcing samples Then we update activity recognition models with the discovered differences A mobile can correctly utilize crowdsourcing samples for recognition model construction with MEMAR and can also track and recognize mobile users’ behavior dynamics The experiments based on a smartphone dataset verify the validity of MEMAR We believe MEMAR provides a basis for context-aware mobile applications

Feedback-based metric learning for activity recognition

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Heuristic routing algorithm toward scalable distributed generalized assignment problem

Feedback-based metric learning for activity recognition