Network interoperability is crucial for achieving seamless communication across Internet of Things (IoT) environments. IoT comprises heterogeneous devices and systems supporting diverse technologies, protocols, and manufacturers. Enabling devices to communicate and exchange data effectively, regardless of underlying protocols, is key to building cohesive and integrated IoT networks. IoT has transformed multiple sectors ranging from home automation to healthcare—by harnessing a vast array of sensors and actuators that communicate through cloud, fog, and edge layers. However, the variety in device manufacturing and communication standards demands interoperable interfaces, and most current solutions depend on cloud-based centralised architectures. These architectures introduce latency and scalability challenges, particularly for resource-constrained IoT devices that often struggle to communicate with the cloud due to limited resources. This paper addresses network interoperability at the IoT edge level, focusing on resource-efficient communication by integrating Wi-Fi and Bluetooth, two commonly used protocols in IoT ecosystems. We have implemented a network edge interoperability solution that supports effective data exchange between devices operating on these distinct protocols, enhancing the overall efficiency, flexibility, and scalability of IoT systems. Our approach allows devices interoperate by addressing network latency and bandwidth limitations, incorporating an integrated controller to facilitate broader applications and enhance performance across IoT networks. Our findings illustrate how bridging protocol differences can foster more resilient and adaptable IoT solutions, advancing the deployment of IoT applications across various domains and use cases.

The Internet of Things (IoT) has many important applications in multiple domains that include home automation, smart cites, healthcare, agriculture, and environment. IoT comprises a wide range of sensors and actuators that communicate with each other over cloud, fog and edge level networks. Moreover, these devices use various communication protocols and are made by different manufactures. To deal with these diversities, IoT essentially needs interoperable communication interfaces among devices. Unfortunately, existing interoperability solutions are centralised and use fog or cloud level computing resources, making IoT communications latency-prone and poorly scalable. These issues could be handled effectively, if edge level devices could be made interoperable within the edge level and without needing fog or cloud level access. This paper proposes a decentralized interoperability solution that stays fully within the edge level. The solution relies on controller devices that work on the interface boundaries of the edge devices. Unlike existing solutions, the proposed solution adopts a hierarchical interoperability model to handle interoperability at network, syntactical, semantic, and organizational levels. Our solution is non-proprietary, generic over vendors and platforms, and easily extendable to new devices. We compare our proposed solution with existing interoperability solutions for edge devices and show its mobility, efficiency and flexibility.

MOTIVATION: Protein backbone angle prediction has achieved significant accuracy improvement with the development of deep learning methods. Usually the same deep learning model is used in making prediction for all residues regardless of the categories of secondary structures they belong to. In this paper, we propose to train separate deep learning models for each category of secondary structures. Machine learning methods strive to achieve generality over the training examples and consequently loose accuracy. In this work, we explicitly exploit classification knowledge to restrict generalisation within the specific class of training examples. This is to compensate the loss of generalisation by exploiting specialisation knowledge in an informed way. RESULTS: The new method named SAP4SS obtains mean absolute error (MAE) values of 15.59, 18.87, 6.03, and 21.71 respectively for four types of backbone angles [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]. Consequently, SAP4SS significantly outperforms existing state-of-the-art methods SAP, OPUS-TASS, and SPOT-1D: the differences in MAE for all four types of angles are from 1.5 to 4.1% compared to the best known results. AVAILABILITY: SAP4SS along with its data is available from https://gitlab.com/mahnewton/sap4ss .

The travelling thief problem (TTP) is a multi-component optimisation problem involving two interdependent NP-hard components: the travelling salesman problem (TSP) and the knapsack problem (KP). Recent state-of-the-art TTP solvers modify the underlying TSP and KP solutions in an iterative and interleaved fashion. The TSP solution (cyclic tour) is typically changed in a deterministic way, while changes to the KP solution typically involve a random search, effectively resulting in a quasi-meandering exploration of the TTP solution space. Once a plateau is reached, the iterative search of the TTP solution space is restarted by using a new initial TSP tour. We propose to make the search more efficient though an adaptive surrogate model (based on a customised form of Support Vector Regression) that learns the characteristics of initial TSP tours that lead to good TTP solutions. The model is used to filter out non-promising initial TSP tours, in effect reducing the amount of time spent to find a good TTP solution. Experiments on a broad range of benchmark TTP instances indicate that the proposed approach filters out a considerable number of non-promising initial tours, at the cost of missing only a small number of the best TTP solutions.

The travelling thief problem (TTP) is a combination of two interdependent NP-hard components: travelling salesman problem (TSP) and knapsack problem (KP). Existing approaches for TTP typically solve the TSP and KP components in an interleaved fashion, where the solution to one component is held fixed while the other component is changed. This indicates poor coordination between solving the two components and may lead to poor quality TTP solutions. For solving the TSP component, the 2-OPT segment reversing heuristic is often used for modifying the tour. We propose an extended and modified form of the reversing heuristic in order to concurrently consider both the TSP and KP components. Items deemed as less profitable and picked in cities earlier in the reversed segment are replaced by items that tend to be equally or more profitable and not picked in the later cities. Comparative evaluations on a broad range of benchmark TTP instances indicate that the proposed approach outperforms existing state-of-the-art TTP solvers.

We describe a constraint-based automated planner named Transition Constraints for Parallel Planning (TCPP). TCPP constructs its constraint model from a redefined version of the domain transition graphs (DTG) of a given planning problem. TCPP encodes state transitions in the redefined DTGs by using table constraints with cells containing don't cares or wild cards. TCPP uses Minion the constraint solver to solve the constraint model and returns a parallel plan. We empirically compare TCPP with the other state-of-the-art constraint-based parallel planner PaP2. PaP2 encodes action successions in the finite state automata (FSA) as table constraints with cells containing sets of values. PaP2 uses SICStus Prolog as its constraint solver. We also improve PaP2 by using don’t cares and mutex constraints. Our experiments on a number of standard classical planning benchmark domains demonstrate TCPP's efficiency over the original PaP2 running on SICStus Prolog and our reconstructed and enhanced versions of PaP2 running on Minion.

We present a planner named Transition Constraints for Parallel Planning (TCPP). TCPP constructs a new constraint model from domain transition graphs (DTG) of a given planning problem. TCPP encodes the constraint model by using table constraints that allow don't cares or wild cards as cell values. TCPP uses Minion the constraint solver to solve the constraint model and returns the parallel plan. Empirical results exhibit the efficiency of our planning system over state-of-the-art constraint-based planners.

In this paper, we present a formal definition of the integration of the requirements modeling language Behavior Trees (BTs). We first provide the semantic integration of two interrelated BTs using an extended version of Communicating Sequential Processes. We then use a Semantic Network Model to capture a set of interrelated BTs, and develop algorithm to integrate them all into one BT. This formalisation facilitates developing (semi-)automated tools for modeling the requirements of large-scale software intensive systems.

Protein structure prediction is an unsolved problem in computational biology. One great difficulty is due to the unknown factors in the actual energy function. Moreover, the energy models available are often not very informative particularly when spatially similar structures are compared during search. We introduce several novel heuristics to augment the energy model and present a new local search algorithm that exploits these heuristics in a mixed fashion. Although the heuristics individually are weaker in performance than the energy function, their combination interestingly produces stronger results. For standard benchmark proteins on the face centered cubic lattice and a realistic 20x20 energy model, we obtain structures with significantly lower energy than those obtained by the state-of-the-art algorithms. We also report results for these proteins using the same energy model on the cubic lattice.

In this paper, we present a local search framework for lattice fit problem of proteins. Our algorithm significantly improves state-of-the-art results and justifies the significance of the lattice models. In addition to these, our analysis reveals the weakness of several energy functions used.

No single algorithm suits the best for the protein structure prediction problem. Therefore, researchers have tried hybrid techniques to mix the power of different strategies to gain improvements. In this paper, we present a hybrid search framework that embeds a tabu-based local search within a population based genetic algorithm. We applied our hybrid algorithm on simplified protein structure prediction problem. We use a low-resolution ab initio search method with the hydrophobic-polar energy model and face-centred-cubic lattice. Within the genetic algorithm, we apply local search in two different situations: i) only once at the beginning and ii) every time at search stagnation. At the beginning, we apply local search to improve the randomly generated individuals and use them as an initial population for the genetic algorithm. Later, we apply local search after applying a random-walk at situations where the genetic algorithm gets stuck. In both cases, the use of local search is to improve the randomised solutions quickly. We experimentally show that our hybrid approach outperforms the state-of-the-art approaches.

Protein structure prediction is one of the most challenging problems in computational biology and remains unsolved for many decades. In a simplified version of the problem, the task is to find a self-avoiding walk with the minimum free energy assuming a discrete lattice and a given energy matrix. Genetic algorithms currently produce the state-of-the-art results for simplified protein structure prediction. However, performance of the genetic algorithms largely depends on the encodings they use in representing protein structures and the twin removal technique they use in eliminating duplicate solutions from the current population. In this paper, we present a new efficient encoding for protein structures. Our encoding is nonisomorphic in nature and results into efficient twin removal. This helps the search algorithm diversify and explore a larger area of the search space. In addition to this, we also propose an approximate matching scheme for removing near-similar solutions from the population. Our encoding algorithm is generic and applicable to any lattice type. On the standard benchmark proteins, our techniques significantly improve the state-of-the-art genetic algorithm for hydrophobic-polar (HP) energy model on face-centered-cubic (FCC) lattice.

BACKGROUND: Protein structure prediction is an important but unsolved problem in biological science. Predicted structures vary much with energy functions and structure-mapping spaces. In our simplified ab initio protein structure prediction methods, we use hydrophobic-polar (HP) energy model for structure evaluation, and 3-dimensional face-centred-cubic lattice for structure mapping. For HP energy model, developing a compact hydrophobic-core (H-core) is essential for the progress of the search. The H-core helps find a stable structure with the lowest possible free energy. RESULTS: In order to build H-cores, we present a new Spiral Search algorithm based on tabu-guided local search. Our algorithm uses a novel H-core directed guidance heuristic that squeezes the structure around a dynamic hydrophobic-core centre. We applied random walks to break premature H-cores and thus to avoid early convergence. We also used a novel relay-restart technique to handle stagnation. CONCLUSIONS: We have tested our algorithms on a set of benchmark protein sequences. The experimental results show that our spiral search algorithm outperforms the state-of-the-art local search algorithms for simplified protein structure prediction. We also experimentally show the effectiveness of the relay-restart.

BACKGROUND: Given a protein's amino acid sequence, the protein structure prediction problem is to find a three dimensional structure that has the native energy level. For many decades, it has been one of the most challenging problems in computational biology. A simplified version of the problem is to find an on-lattice self-avoiding walk that minimizes the interaction energy among the amino acids. Local search methods have been preferably used in solving the protein structure prediction problem for their efficiency in finding very good solutions quickly. However, they suffer mainly from two problems: re-visitation and stagnancy. RESULTS: In this paper, we present an efficient local search algorithm that deals with these two problems. During search, we select the best candidate at each iteration, but store the unexplored second best candidates in a set of elite conformations, and explore them whenever the search faces stagnation. Moreover, we propose a new non-isomorphic encoding for the protein conformations to store the conformations and to check similarity when applied with a memory based search. This new encoding helps eliminate conformations that are equivalent under rotation and translation, and thus results in better prevention of re-visitation. CONCLUSION: On standard benchmark proteins, our algorithm significantly outperforms the state-of-the art approaches for Hydrophobic-Polar energy models and Face Centered Cubic Lattice.

In this paper we present a multi-key index model that enables us to search a record with more than one attribute values in distributed database systems. Indices provide fast and efficient access of data and so become a major aspect in centralized database systems. Most of the centralized database systems use B + tree or other types of index structures such as bit vector, graph structure, grid file etc. But in distributed database systems no index model is found in the literature. Therefore efficient access is a major problem in distributed databases. Our proposed index model avoids the query-flooding problem of existing system and thus optimizes network bandwidth.