The accurate positioning of location in indoor environment has become crucial in many location-based services, mainly where global positioning systems (GPSs) are unavailable or fail to navigate correctly. Conventional fingerprint-based approaches face challenges with instability, low accuracy, and being sensitive to changes in the environment. This study proposes a robust fingerprint-based machine learning (ML) model for dynamic environment indoor navigation in real time. The proposed model uses link quality indicator (LQI) values from IEEE 802.15.4 as fingerprints and supervised learning algorithms, showing high accuracy and a strong ability to adapt to changes in the environment. A room within a building floor has been regarded as the unit of location identification instead of the user’s exact coordinates to make the suggested model more relevant under practical conditions. The model was trained and tested using a real LQI dataset collected from varied indoor conditions to ensure the system can adapt effectively and operate consistently in dynamic environments and signal conditions. The results show that the proposed model surpasses fingerprinting indoor navigation in room detection accuracy and flexibility to environmental changes. An implemented prototype proved the real-time capability of the proposal in smart buildings, hospitals, and industrial IoT settings.

Cotton is known as ‘white gold’ in the agricultural industry. Agriculture is the primary source of economic income in Bangladesh and the country's economy is heavily dependent on agriculture. The soil and water resources of our country are fertile and the climate is moderate. But numerous diseases affect crop production and cause enormous crop losses, endangering the lives of helpless farmers. A previous report showed that about 70–80% of cotton diseases were leaf diseases and 30–20% were pest diseases. Experts typically use bare eyes to find and identify such plant diseases and pests which may result from lower accuracy of the identification. As a result, early detection of cotton disease using AI-based systems may help to increase the production of cotton by detecting the leaf disease significantly. In this research, we proposed a DL-based cotton leaf disease detection approach using fine-tuning Transfer Learning (TL) algorithms by tuning the layers and parameters of the existing TL algorithms. We also investigated the performance of several fine-tuning TL models such as VGG-16, VGG-19, Inception-V3 and Xception on the publicly available cotton dataset for cotton disease prediction. The investigations found that the Xception model provides the highest accuracy rate of 98.70% and was selected to develop a web-based smart application for real-life cotton disease prediction in farming to increase cotton production. Hence, our model can accurately diagnose cotton leaf diseases and will provide a new window for the automatic leaf disease diagnosis of other plants.

Association rules mining is a major data mining field that leads to discovery of associations and correlations among items in today's big data environment.The conventional association rule mining focuses mainly on positive itemsets generated from frequently occurring itemsets (PFIS).However, there has been a significant study focused on infrequent itemsets with utilization of negative association rules to mine interesting frequent itemsets (NFIS) from transactions.In this work, we propose an efficient backward calculating negative frequent itemset algorithm namely EBC-NFIS for computing backward supports that can extract both positive and negative frequent itemsets synchronously from dataset.EBC-NFIS algorithm is based on popular e-NFIS algorithm that computes supports of negative itemsets from the supports of positive itemsets.The proposed algorithm makes use of previously computed supports from memory to minimize the computation time.In addition, association rules, i.e. positive and negative association rules (PNARs) are generated from discovered frequent itemsets using EBC-NFIS algorithm.The efficiency of the proposed algorithm is verified by several experiments and comparing results with e-NFIS algorithm.The experimental results confirm that the proposed algorithm successfully discovers NFIS and PNARs and runs significantly faster than conventional e-NFIS algorithm.

: Nowadays, wireless local area network (WLAN) has become preva-lent Internet access due to its low-cost gadgets, flexible coverage and hassle-free simple wireless installation. WLAN facilitates wireless Internet services to users with mobile devices like smart phones, tablets, and laptops through deployment of multiple access points (APs) in a network field. Every AP operates on a frequency band called channel. Popular wireless standard such as IEEE 802.11n has a limited number of channels where frequency spectrum of adjacent channels overlaps partially with each other. In a crowded environ-ment, users may experience poor Internet services due to channel collision i.e., interference from surrounding APs that affects the performance of the WLAN system. Therefore, it becomes a challenge to maintain expected performance in a crowded environment. A mathematical model of throughput considering interferences from surrounding APs can play an important role to set up a WLAN system properly. While set up, assignment of channels considering interference can maximize network performance. In this paper, we investigate the signal propagation of APs under interference of partially overlapping channels for both bonded and non-bonded channels. Then, a throughput estimation model is proposed using difference of operating channels and received signal strength indicator (RSSI). Then, a channel assignment algorithm is introduced using proposed throughput estimation model. Finally, the efficiency of the proposal is verified by numerical experiments using simulator. The results show that the proposal selects the best channel combination of bonded and non-bonded channels that maximize the performance.

With the increasing reliance on technology, it has become crucial to secure every aspect of online information where pseudo random binary sequences (PRBS) can play an important role in today’s world of Internet. PRBS work in the fundamental mathematics behind the security of different protocols and cryptographic applications. This paper proposes a new PRBS namely MK (Mamun, Kumu) sequence for security applications. Proposed sequence is generated by primitive polynomial, cyclic difference set in elements of the field and binarized by quadratic residue (QR) and quadratic nonresidue (QNR). Introduction of cyclic difference set makes a special contribution to randomness of proposed sequence while QR/QNR-based binarization ensures uniformity of zeros and ones in sequence. Besides, proposed sequence has maximum cycle length and high linear complexity which are required properties for sequences to be used in security applications. Several experiments are conducted to verify randomness and results are presented in support of robustness of the proposed MK sequence. The randomness of proposed sequence is evaluated by popular statistical test suite, i.e., NIST STS 800-22 package. The test results confirmed that the proposed sequence is not affected by approximations of any kind and successfully passed all statistical tests defined in NIST STS 800-22 suite. Finally, the efficiency of proposed MK sequence is verified by comparing with some popular sequences in terms of uniformity in bit pattern distribution and linear complexity for sequences of different length. The experimental results validate that the proposed sequence has superior cryptographic properties than existing ones.

Three linksThe IEEE 802.11n wireless local-area network (WLAN) has been extensively deployed around the world due to the flexibility, lower cost, and the high-speed data transmission capability at 2.4 GHz ISM band.However, in the WLAN deployment, one key challenge is to optimize the channel assignment of access-points (APs) under the small number of partially overlapping channels (POCs) to reduce radio interference, particularly for the channel bonding.In POCs, the frequency spectrums of adjacent channels are partially overlapped with one another, which will result to low throughput for concurrently communicating links using them.The accurate throughput estimation of a link is critical in the optimal WLAN deployment.Previously, we studied the throughput drop estimation model using the receiving signal strength (RSS) from the interfered link for two concurrently communicating links under POCs.In this paper, based on measurement results, we have extended this model for three concurrently communicating links.The accuracy of this model extension is verified by comparing the estimated results with the measured ones.

A Wireless Internet-access Mesh NETwork (WIMNET) provides scalable and reliable internet access through the deployment of multiple access points (APs) and gateways (GWs). In this work, we propose a selective routing algorithm aiming at a hierarchical minimization of the operational cost and the maximal end-to-end delay. In particular, by deploying redundant APs/GWs in the network field, the WIMNET becomes robust to the link or AP/GW failure. However, these redundant APs/GWs increase the operational cost like the power consumption. By using Dijkstra algorithm and 2-opt algorithm, the proposed algorithm iteratively deactivates the deployed APs/GWs and performs the routing that reduces the maximal end-to-end delay based on the APs/GWs remaining active. The generated route meets the real-world constraints like fairness criterion. We further propose a cross-layer design to enhance the routing performance by exploiting the MAC-layer frame aggregation technique. The selective routing algorithm is then implemented in the WIMNET simulator proposed by our group. The numerical experiments demonstrate that in both indoor and open space environments, the proposed selective routing greatly reduces the operational cost, i.e., up to \(80\%\) APs/GWs can be deactivated.

Recently, a wireless local-area networks (WLANs) has become prevalent around the world due to the low-cost and flexible internet access by wireless communications between user hosts and access points (APs) in the network. Previously, we proposed the active AP configuration algorithm for the elastic WLAN system that dynamically optimises the network configuration by activating/deactivating APs and assigning the channels and associated hosts depending on network situations. However, it assumed that only single-input-single-output (SISO) links are used, although multiple-input-multiple-output (MIMO) links have become popular for fast communications. Currently, most commercial APs can use MIMO, while a subset of hosts can use it, referred as MIMO hosts in this paper. Then, by optimising the locations of MIMO hosts, the performance of the network is expected to be improved. In this paper, we present the MIMO host location optimisation as the extension of the active AP configuration algorithm. Through simulations in two network topologies using the WIMNET simulator, the throughput improvements by this proposal are confirmed.

Recently, Wireless Local Area Networks (WLANs) have increased popularity around the world, where users can easily access to the Internet through associations with access points (APs) using mobile devices like smart phones, tablets, and laptops. In a WLAN, it is common that the number of users is always changing by time and users are not evenly distributed in the field. To optimize the number of active APs and the host associations in the network depending on traffic demands from users, previously, we proposed the AP configuration algorithm for the elastic WLAN system. Unfortunately, this algorithm can find the solution for the fixed user state in the network, although users often repeat joining and leaving the network. In this paper, we propose the extension of the AP configuration algorithm to deal with this dynamic nature. Here, as practical situations, this extension considers that at most one host may join or leave at the same time, and any communicating AP cannot be suspended and any communicating host cannot change its associated AP. Through numerical experiments using the WIMNET simulator in two network instances, the effectiveness of the proposal is demonstrated. Furthermore, it is implemented in the elastic WLAN system testbed using Raspberry Pi for the AP. The performance of this implementation is verified through experiments in four scenarios.

Recently, Wireless Local Area Networks (WLANs) have increased popularity around the world, because users can easily access to the Internet through associations with access points (APs), using mobile devices like smart phones, tablets, and laptops. In a WLAN network, it is common that the number of users is always changing by time and they are not evenly distributed in the field. To optimize the number of active APs and the host associations in the network depending on traffic demands from users, previously, we proposed the AP configuration algorithm for the elastic WLAN system. Unfortunately, this algorithm can find the solution for the fixed user state in the network, although it is usual that users repeat joining and leaving the network. In this paper, we propose the extension of the AP configuration algorithm to deal with this dynamic nature of the WLAN network. Here, it is considered that at most one host may join or leave at the same time, and any communicating AP cannot be suspended and any communicating host cannot change its associated AP. Through numerical experiments using the WIMNET simulator in two network instances, the effectiveness of the proposal is demonstrated.

Recently, an IEEE 802.11n access point (AP) prevailed over the wireless local area network (WLAN) due to the high-speed data transmission using the multiple input multiple output (MIMO) technology.Unfortunately, the signal propagation from the 802.11nAP is not uniform in the circumferential and height directions because of the multiple antennas for MIMO.As a result, the data transmission speed between the AP and a host could be significantly affected by their relative setup conditions.In this paper, we propose a minimax approach for optimizing the 802.11nAP setup condition in terms of the angles and the height in an indoor environment using throughput measurements.First, we detect a bottleneck host that receives the weakest signal from the AP in the field using the throughput estimation model.To explore optimal values of parameters for this model, we adopt the versatile parameter optimization tool.Then, we optimize the AP setup by changing the angles and the height while measuring throughput.For evaluations, we verify the accuracy of the model using measurement results and confirm the throughput improvements for hosts in the field by our approach.

Keyphrases are set of words that reflect the main topic of interest of a document.It plays vital roles in document summarization, text mining, and retrieval of web contents.As it is closely related to a document, it reflects the contents of the document and acts as indices for a given document.Extracting the ideal keyphrases is important to understand the main contents of the document.In this work, we present a keyphrase extraction method that efficiently finds the keywords from English documents.The methods use some important features of the document such as TF, TF*IDF, GF, GF*IDF, TF*GF*IDF for the purpose.Finally, the performance of the proposal is evaluated using wellknown document corpus.

The pseudo random binary sequence plays an important role in cryptography and network security system. This paper proposes a new approach to pseudo random binary sequence over finite field and evaluates some important properties of the newly generated sequence. The sequence is generated by applying a primitive polynomial over finite field, trace function and modified mobius function. Then period, autocorrelation, cross-correlation and linear complexity properties of the generated sequence have been presented in this paper. Finally, the proposed sequence is analyzed on various bit length of odd characteristics of field and compared with some existing works. The comparison results show the superiority of the proposed approach over existing works in terms of its properties.

Previously, we proposed the active access-point (AP) configuration algorithm for elastic Wireless Local-Area Network (WLAN) systems using heterogeneous APs. This algorithm activates or deactivates APs depending on traffic demands, assuming that any active AP can use a different channel to avoid interferences among APs. However, the number of non-interfered channels in IEEE 802.11 protocols is limited. In this paper, we propose an extension of this algorithm to consider the channel assignment to the active APs under this limitation. After the channel assignment to the active APs, AP associations of hosts are improved to further minimize interferences by averaging loads among different channels. The effectiveness of our proposal is evaluated using the WIMNET simulator.

An Elastic Wireless Local-Area Network (WLAN) system provides a reliable, flexible, and efficient Internet access to users through installations of heterogeneous access points (APs) including dedicated APs (DAPs), virtual APs (VAPs), and mobile APs (MAPs). The number of APs should be carefully selected to optimize the network performance. Specifically, for heavy traffic, a large number of APs are required. However, the dense deployment of APs introduces the inter-AP interferences which may eventually degrade the communication quality when the number of users are few. In this paper, we propose an active access-point configuration algorithm that activates or deactivates APs according to the changes of network topologies and demands of users for the elastic WLAN system. The algorithm considers the bandwidth difference among heterogeneous AP devices and the total available bandwidth in the network. The number of active APs is minimized to ensure the minimum inter-AP interference subject to the constraints. The host locations can be the candidate positions for the MAPs, because host owners may use them for the Internet access. The effectiveness of the proposed algorithm is demonstrated using the WIMNET simulator.

Recently, an IEEE802.11n access point (AP) has become common in the wireless local-area network (WLAN) due to the high-speed data transmission using the multiple input multiple output (MIMO) technology. However, the signal propagation from the 802.11n AP may not be uniform in the circumferential direction because of multiple antennas in MIMO, in addition to the height direction. As a result, the data transmission speed between the AP and a host can be significantly affected by their relative setup conditions. In this paper, we propose a minimax approach for optimizing the 802.11n AP setup condition in terms of the height and the angles in an indoor environment using throughput measurements. First, we detect a bottleneck host that receives the weakest signal from the AP in the field using the throughput estimation model. Then, we optimize the AP setup by changing the height and directions while measuring throughputs. For evaluations, we confirm the accuracy of the model using measurement results and the throughput improvements at most hosts including the bottleneck one in the field by our approach.