Digital Pheromones Research Articles

The digital pheromone: Building digital identity of smartphone users based on time-varying multivariates

The increasing reliance on smartphones necessitates secure, accurate user identification methods. This study introduces “digital pheromones”, a novel digital identity creation method based on smartphone user behaviors. Represented by a 56-bit binary number, this identity is derived from quantization and concatenation of 14 time-varying smartphone variables. Motivated by the need for enhanced security and privacy, the study employs statistical analysis to measure user behavior dispersion, showing that wider dispersion variables facilitate faster user uniqueness at lower quantization levels. The results demonstrate 100% uniqueness, ensuring no two users share the same 56-bit identity. This research offers a robust framework for future advancements in smartphone user authentication and identification systems.

Scalable Task Allocation with Communications Connectivity for Flying Ad-Hoc Networks

Task allocation enables heterogeneous agents to execute heterogeneous tasks in the domain of unmanned aerial vehicles, while responding to dynamic changes in the environment and available resources to complete complex, multi-objective missions, leading to swarm intelligence. We propose a bio-inspired approach using digital pheromones to perform scalable task allocation when the number of agents, tasks, and the diameter of the communications graph increase. The resulting emergent behaviour also enables idle agents in the swarm to provide periodic or continuous connectivity between disconnected parts of the swarm. We validate our results through simulation and demonstrate the feasibility of our approach by applying it to the 3D coverage and patrol problem.

Design of a UAV for Autonomous RFID-Based Dynamic Inventories Using Stigmergy for Mapless Indoor Environments

Unmanned aerial vehicles (UAVs) and radio frequency identification (RFID) technology are becoming very popular in the era of Industry 4.0, especially for retail, logistics, and warehouse management. However, the autonomous navigation for UAVs in indoor map-less environments while performing an inventory mission is, to this day, an open issue for researchers. This article examines the method of leveraging RFID technology with UAVs for the problem of the design of a fully autonomous UAV used for inventory in indoor spaces. This work also proposes a solution for increasing the performance of the autonomous exploration of inventory zones using a UAV in unexplored warehouse spaces. The main idea is to design an indoor UAV equipped with an onboard autonomous navigation system called RFID-based stigmergic and obstacle avoidance navigation system (RFID-SOAN). RFID-SOAN is composed of a computationally low cost obstacle avoidance (OA) algorithm and a stigmergy-based path planning and navigation algorithm. It uses the same RFID tags that retailers add to their products in a warehouse for navigation purposes by using them as digital pheromones or environmental clues. Using RFID-SOAN, the UAV computes its new path and direction of movement based on an RFID density-oriented attraction function, which estimates the optimal path through sensing the density of previously unread RFID tags in various directions relative to the pose of the UAV. We present the results of the tests of the proposed RFID-SOAN system in various scenarios. In these scenarios, we replicate different typical warehouse layouts with different tag densities, and we illustrate the performance of the RFID-SOAN by comparing it with a dead reckoning navigation technique while taking inventory. We prove by the experiments results that the proposed UAV manages to adequately estimate the amount of time it needs to read up-to 99.33% of the RFID tags on its path while exploring and navigating toward new zones of high populations of tags. We also illustrate how the UAV manages to cover only the areas where RFID tags exist, not the whole map, making it very efficient, compared to the traditional map/way-points-based navigation.

Swarm intelligence-based green optimization framework for sustainable transportation

Traffic congestion is one of the most critical issues in developing sustainable transportation in smart cities. As the Internet of Things evolves, connected vehicle technology has arisen as an essential research topic in smart, sustainable transportation. This study investigates a decentralized green traffic optimization framework by pushing swarm intelligence into connected vehicles to mitigate traffic congestion. We present a dynamic traffic routing method based on ant species’ swarm intelligence for connected vehicles so that they can communicate with each other and their surrounding environment via digital pheromones to perform routing decision-making in a decentralized manner. Traditional pheromones attract other vehicles to move to the optimal path, which will soon be congested if many vehicles travel on that path concurrently. To overcome this limitation, we propose the concept of repelling pheromone, which generates a repulsive force among vehicles so that their travel paths are distributed throughout a road network, resulting in a congestion-free road network. The proposed method is validated in the Simulation of Urban Mobility platform. Simulation findings reveal that the proposed method outperforms baseline methods in mitigating traffic congestion, reducing average fuel consumption and emissions by 13–19% and the average trip duration by 19–28%.

Target detection for multi-UAVs via digital pheromones and navigation algorithm in unknown environments

Coordinating multiple unmanned aerial vehicles (multi-UAVs) is a challenging technique in highly dynamic and sophisticated environments. Based on digital pheromones as well as current mainstream unmanned system controlling algorithms, we propose a strategy for multi-UAVs to acquire targets with limited prior knowledge. In particular, we put forward a more reasonable and effective pheromone update mechanism, by improving digital pheromone fusion algorithms for different semantic pheromones and planning individuals’ probabilistic behavioral decision-making schemes. Also, inspired by the flocking model in nature, considering the limitations of some individuals in perception and communication, we design a navigation algorithm model on top of Olfati-Saber’s algorithm for flocking control, by further replacing the pheromone scalar to a vector. Simulation results show that the proposed algorithm can yield superior performance in terms of coverage, detection and revisit efficiency, and the capability of obstacle avoidance.

A Unified Framework for Vehicle Rerouting and Traffic Light Control to Reduce Traffic Congestion

As the number of vehicles grows rapidly each year, more and more traffic congestion occurs, becoming a big issue for civil engineers in almost all metropolitan cities. In this paper, we propose a novel pheromone-based traffic management framework for reducing traffic congestion, which unifies the strategies of both dynamic vehicle rerouting and traffic light control. Specifically, each vehicle, represented as an agent, deposits digital pheromones over its route, while roadside infrastructure agents collect the pheromones and fuse them to evaluate real-time traffic conditions as well as to predict expected road congestion levels in near future. Once road congestion is predicted, a proactive vehicle rerouting strategy based on global distance and local pheromone is employed to assign alternative routes to selected vehicles before they enter congested roads. In the meanwhile, traffic light control agents take online strategies to further alleviate traffic congestion levels. We propose and evaluate two traffic light control strategies, depending on whether or not to consider downstream traffic conditions. The unified pheromone-based traffic management framework is compared with seven other approaches in simulation environments. Experimental results show that the proposed framework outperforms other approaches in terms of traffic congestion levels and several other transportation metrics, such as air pollution and fuel consumption. Moreover, experiments over various compliance and penetration rates show the robustness of the proposed framework.

A study of graphics hardware accelerated particle swarm optimization with digital pheromones

Programmable Graphics Processing Units (GPUs) have lately become a promising means to perform scientific computations. Modern GPUs have proven to outperform the number of floating point operations when compared to traditional Central Processing Units (CPUs) through inherent data parallel architecture and higher bandwidth capabilities. They allow scientific computations to be performed without noticeable degradation in accuracy in a fraction of the time compared to traditional CPUs at substantially reduced costs, making them viable alternatives to expensive computer clusters or workstations. GPU programmability however, has fostered the development of a variety of programming languages making it challenging to select a computing language and use it consistently without the pitfall of being obsolete. Some GPU languages are hardware specific and are designed to rake in performance boosts when used with their host GPUs (e.g., Nvidia Cuda). Others are operating system specific (e.g., Microsoft HLSL). A few are platform agnostic lending themselves to be used on a workstation with any CPU and a GPU (e.g., GLSL, OpenCL). Of a number of companies and organizations that implement formal optimization into their processes, only a few utilize GPUs. It is either because the others are either vested much into CPU based computing or they are not fully aware of the benefits of implementing population based optimization routines in GPUs. Literature shows a large number of research publications specifically in the field of optimization utilizing GPUs. However, most of them are limited to a specific GPU hardware or addressed specific problems. The diversity in current GPU hardware and software APIs present overwhelming number of choices making it challenging to decide where and how to begin transitioning to GPU based computing, impeding promising computing avenues that relatively is very cost effective. In this paper, the authors precisely intend to address some of these issues by broadly classifying GPU APIs into three categories: 1) Hardware vendor dependent GPU APIs, 2) Graphical in context APIs, and 3) Platform agnostic APIs. Prior work by the authors demonstrated the capability of digital pheromones within Particle Swarm Optimization (PSO) for searching n-dimensional design spaces with improved accuracy, efficiency and reliability in serial and parallel CPU computing environments. To study the impact of GPUs, the authors have taken this digital pheromone variant of PSO and implemented it on three GPU APIs, each representing a category listed above, in a simplistic sense --- delegate unconstrained explicit objective function evaluations to GPUs. While this approach itself cannot be considered novel, the takeaways from implementing it on different GPU APIs provided a wealth of information that the authors believe can help optimization companies and organizations make informed decisions in implementing GPUs in their processes.

Exploiting spatio-temporal patterns using partial-state reinforcement learning in a synthetically augmented environment

Responding to or anticipating a sequence of events caused by adversarial human actors, such as crimes, can be a difficult task. Reinforcement learning has not been highly utilized as a method for positioning agents to respond to such events. In our earlier work, which was applied to positioning naval vessel agents to respond to Somali maritime piracy attacks, we developed a method to synthetically augment the information in the events’ environment with digital pheromones and other information augmenters, used the resulting augmenter signatures as states that agents could react to, and applied reinforcement learning to exploit regularities in the timing and location of events to position agents in spatio-temporal proximity of anticipated events. This work extends that methodology with a new learning boosting method wherein learning is improved as partial augmenter signatures are reinforced, which is not possible when learning is based only on the aggregated state. The enhanced methodology is applied to positioning police patrols in response to a sequence of business robberies in Denver, Colorado and its effectiveness is analyzed.

Reinforcement Learning in an Environment Synthetically Augmented with Digital Pheromones

Reinforcement learning requires information about states, actions, and outcomes as the basis for learning. For many applications, it can be difficult to construct a representative model of the environment, either due to lack of required information or because of that the model's state space may become too large to allow a solution in a reasonable amount of time, using the experience of prior actions. An environment consisting solely of the occurrence or nonoccurrence of specific events attributable to a human actor may appear to lack the necessary structure for the positioning of responding agents in time and space using reinforcement learning. Digital pheromones can be used to synthetically augment such an environment with event sequence information to create a more persistent and measurable imprint on the environment that supports reinforcement learning. We implemented this method and combined it with the ability of agents to learn from actions not taken, a concept known as fictive learning. This approach was tested against the historical sequence of Somali maritime pirate attacks from 2005 to mid-2012, enabling a set of autonomous agents representing naval vessels to successfully respond to an average of 333 of the 899 pirate attacks, outperforming the historical record of 139 successes.

PARALLEL IMPLEMENTATION OF CROSS-LAYER OPTIMIZATION - A PERFORMANCE EVALUATION BASED ON SWARM INTELLIGENCE

In distributed systems real time optimizations need to be performed dynamically for better utilization of the network resources. Real time optimizations can be performed effectively by using Cross Layer Optimization (CLO) within the network operating system. This paper presents the performance evaluation of Cross Layer Optimization (CLO) in comparison with the traditional approach of Single-Layer Optimization (SLO). In the parallel implementation of the approaches the experimental study carried out indicates that the CLO results in a significant improvement in network utilization when compared to SLO. A variant of the Particle Swarm Optimization technique that utilizes Digital Pheromones (PSODP) for better performance has been used here. A significantly higher speed up in performance was observed from the parallel implementation of CLO that used PSODP on a cluster of nodes.

Self-organising congestion evasion strategies using ant-based pheromones

Social insects perform complex, self-organising tasks within a collective by using pheromone-based indirect communication (swarm intelligence). Inspired by these potentials of nature, this concept is possibly also a paradigm for controlling traffic, for collectively recognising, disintegrating and avoiding traffic congestion without central control instances. Vehicles equipped with location and communication technology act like individual insects and virtually deposit digital pheromones on the road indicating the intense of traffic and enabling other vehicles to indirectly benefit from the trail. This study investigates a technical implementation of swarm intelligence applied to the traffic system and evaluates different evasion strategies for vehicles. Using a micro-simulation environment capable of simulating real city networks, various traffic experiments empirically prove the hypothesis of a self-organising effect concerning the traffic flow in pheromone-based systems.

Synchronous parallelization of Particle Swarm Optimization with digital pheromones

In this paper, Particle Swarm Optimization (PSO) using digital pheromones to coordinate swarms within n-dimensional design spaces in a parallel computing environment is presented. Digital pheromones are models simulating real pheromones emitted by insects for communication to indicate suitable food or nesting location. Particle swarms search the design space with digital pheromones aiding communication within the swarm during an iteration to improve search efficiency. Previous work by the authors demonstrated the capability of digital pheromones within PSO for searching the global optimum with improved accuracy, efficiency and reliability in a single processor computing environment. When multiple swarms explore and exploit the design space in a parallel computing environment, the solution characteristics can be further improved. This premise is investigated through deploying swarms on multiple processors in a distributed memory parallel computing environment. The primary hurdle for the developed algorithm was bandwidth latency due to synchronization across processors, causing the solution duration due to each swarm to be only as fast as the slowest participating processor. However, it has been observed that the speedup and parallel efficiency improved substantially as the dimensionality of the problems increased. The development of the method along with results from six test problems is presented.

Asynchronous parallelization of particle swarm optimization through digital pheromone sharing

In this paper, a model for sharing digital pheromones between multiple particle swarms to search n-dimensional design spaces in an asynchronous parallel computing environment is presented. Particle swarm optimization (PSO) is an evolutionary technique used to effectively search multi-modal design spaces. With the aid of digital pheromones, members in a swarm can better communicate with each other to improve search performance. Previous work by the authors demonstrated the capability of digital pheromones within PSO for searching the global optimum in both single and coarse grain synchronous parallel computing environments. In the coarse grain approach, multiple swarms are simultaneously deployed across various processors and synchronization is carried out only when all swarms achieved convergence, in an effort to reduce processor-to-processor communication and network latencies. However, it is theorized that with an appropriate parallelization scheme, the benefits of digital pheromones and communication between swarms can outweigh the network bandwidth latencies resulting in improved search efficiency and accuracy. To explore this idea, a swarm is deployed in the design space across different processors. Through an additional processor, each part of the swarm can communicate with the others. While digital pheromones aid communication within a swarm, the developed parallelization model facilitates communication between multiple swarms resulting in improved search accuracy and efficiency. The development of this method and results from solving several multi-modal test problems are presented.

Improving solution characteristics of particle swarm optimization using digital pheromones

In this paper, a new approach to particle swarm optimization (PSO) using digital pheromones to coordinate swarms within an n-dimensional design space is presented. In a basic PSO, an initial randomly generated population swarm propagates toward the global optimum over a series of iterations. The direction of the swarm movement in the design space is based on an individual particle’s best position in its history trail (pBest), and the best particle in the entire swarm (gBest). This information is used to generate a velocity vector indicating a search direction toward a promising location in the design space. The premise of the research presented in this paper is based on the fact that the search direction for each swarm member is dictated by only two candidates—pBest and gBest, which are not efficient to locate the global optimum, particularly in multi-modal optimization problems. In addition, poor move sets specified by pBest in the initial stages of optimization can trap the swarm in a local minimum or cause slow convergence. This paper presents the use of digital pheromones for aiding communication within the swarm to improve the search efficiency and reliability, resulting in improved solution quality, accuracy, and efficiency. With empirical proximity analysis, the pheromone strength in a region of the design space is determined. The swarm then reacts accordingly based on the probability that this region may contain an optimum. The additional information from pheromones causes the particles within the swarm to explore the design space thoroughly and locate the solution more efficiently and accurately than a basic PSO. This paper presents the development of this method and results from several multi-modal test cases.

Pervasive pheromone-based interaction with RFID tags

Despite the growing interest in pheromone-based interaction to enforce adaptive and context-aware coordination, the number of deployed systems exploiting digital pheromones to coordinate the activities of situated autonomous agents is still very limited. In this article, we present a simple low-cost and general-purpose implementation of a pheromone-based interaction mechanism for pervasive environments. This is realized by making use of RFID tags to store digital pheromones and by having humans or robots spread/sense pheromones by properly writing/reading RFID tags populating the surrounding physical environment. We exemplify and evaluate the effectiveness of our approach via an application for object-tracking. This application allows robots and humans to find forgotten-somewhere objects by following pheromones trails associated with them. In addition, we sketch further potential applications of our approach in pervasive computing scenarios, discuss related work in the area, and identify future research directions.

Effectiveness of Digital Pheromones Controlling Swarming Vehicles in Military Scenarios

The use of digital pheromones for controlling and coordinating swarms of unmanned vehicles has been studied under various conditions. This paper describes experiments that demonstrate their effectiveness in several militarily significant scenarios in simulation. The scenarios are described along with the results comparing the performance of swarming to traditional military tactics. A demonstration was conducted using these same algorithms to coordinate unmanned vehicles in a simulated exercise. Two air vehicles controlled by digital pheromones and four ground robots controlled by a related stigmergic algorithm successfully executed a two-hour multi-mission surveillance, patrol, target acquisition, and tracking scenario. The vehicles were given only high-level instructions, such as “survey this areaandidentifyandtrackanytargets”or“patrolaroundthisconvoy”.Theairvehicleswere abletodynamicallyadapttonewcommandsandcoordinatetheiractionswitheachotherand the ground robots to achieve the objectives. The algorithm’s robustness was demonstrated when it dynamically adjusted to the unplanned failure of one of the ground robots without any operator intervention.

Environment as a first class abstraction in multiagent systems

The current practice in multiagent systems typically associates the environment with resources that are external to agents and their communication infrastructure. Advanced uses of the environment include infrastructures for indirect coordination, such as digital pheromones, or support for governed interaction in electronic institutions. Yet, in general, the notion of environment is not well defined. Functionalities of the environment are often dealt with implicitly or in an ad hoc manner. This is not only poor engineering practice, it also hinders engineers to exploit the full potential of the environment in multiagent systems. In this paper, we put forward the environment as an explicit part of multiagent systems.We give a definition stating that the environment in a multiagent system is a first-class abstraction with dual roles: (1) the environment provides the surrounding conditions for agents to exist, which implies that the environment is an essential part of every multiagent system, and (2) the environment provides an exploitable design abstraction for building multiagent system applications. We discuss the responsibilities of such an environment in multiagent systems and we present a reference model for the environment that can serve as a basis for environment engineering. To illustrate the power of the environment as a design abstraction, we show how the environment is successfully exploited in a real world application. Considering the environment as a first-class abstraction in multiagent systems opens up new horizons for research and development in multiagent systems.

UAV Swarm Control: Calculating Digital Pheromone Fields with the GPU

Our future military force will be complex: a highly integrated mix of manned and unmanned units. These unmanned units could function individually or within a swarm. The readiness of future warfighters to work alongside and utilize these new forces depends on the creation of usable interfaces and training simulators. The difficulty is that current unmanned aerial vehicle (UAV) control interfaces require too much operator attention, and common swarm control methods require expensive computational power. This paper begins with a discussion on how to improve upon current user interfaces and then reviews a swarm control method, the digital pheromone field. This method uses digital pheromones to bias the movements of individual units within a swarm toward areas that are attractive and away from areas that are dangerous or unattractive. Next, a more efficient method for performing pheromone field calculations is introduced, one that harnesses the power of the graphics processing unit (GPU) in today's graphics cards by reshaping the ADAPTIV swarm control algorithm into a form acceptable to the GPU's pipeline [1]. The GPU ADAPTIV implementation is tested in scenarios that involve up to 50,000 virtual UAVs. When compared to its counterpart CPU implementation, the GPU version performed over 30 times faster than the CPU version. This gain translates directly into lower costs for training the future warfighter today and fielding the swarms of tomorrow. Finally, this paper presents a vision of how to combine these new interface ideas and performance enhancements into an effective swarm control interface and training simulator.