Cooperative localization in wireless sensor network (WSN) using biased received signal strength (RSS) measurements is investigated in this letter. In the existing work on cooperative RSS localization, measurements of sensor nodes (including both target-anchor and target-target measurements) are generally assumed bias-free. However, in practice, they may be subject to biases, which directly affect localization accuracy. As a result, the existing localization methods are not applicable any more. In this letter, RSS observation biases are considered as the extra parameters to be estimated as well as locations of target nodes. To overcome the nonconvexity of the maximum likelihood (ML) estimator, semidefinite programming (SDP) is applied with $l_{1}$ and $l_{2}$ norms, respectively. Then, the locations of multiple target nodes and observation biases are simultaneously estimated through convex optimization. Numerical examples demonstrate the performance superiority of the proposed methods compared to the existing bias-free SDP methods for wireless cooperative localization.

Received signal strength (RSS) based techniques are promising for location estimation due to low cost and easy implementation. The RSS measurements can be used to determine a receiver's location given the positions of multiple emitters. Also, the RSS measurements at receivers with known positions can be used to determine the location of an emitter. This paper investigates both 3D receiver navigation and source localization using RSS measurements. In practice, sensor mea9 surements are often biased due to design problems or other effects. However, almost all existing methods deal with bias11 free cases. In contrast, we focus on biased cases. For both receiver navigation and source localization, we propose united- RSS (URSS) and differential-RSS (DRSS) approaches, where URSS estimates the location and sensor bias together while DRSS estimates the location by eliminating the effect of bias. They are based on semidefinite programming (SDP) and constrained least squares (CLS), respectively. The proposed methods avoid the nonconvexity in the maximum likelihood (ML) method. Numerical examples verify the necessity for sensor registration in RSS based location estimation and show better performance of the proposed methods.

Gastric cancer (GC) is the most common gastrointestinal malignancy worldwide. However, the molecular mechanisms of the progression of GC are not fully understood. Ras-responsive element binding protein 1 (RREB1) is an oncogene in many types of cancer that is involved in various biological processes, such as DNA damage repair, cell growth and proliferation, cell differentiation, fat development, and fasting glucose balance. In this study, we demonstrate the role of RREB1 in gastric cancer. First, by immunohistochemistry staining (IHC) and bioinformatics analysis, we demonstrated the expression of RREB1 in gastric cancer and paired normal gastric tissues. Then, we established RREB1 overexpression and knockdown cell lines via lentiviral transfection and detected cell proliferation by using MTT, colony-forming, cell cycle and apoptosis assays in vitro. We demonstrated the effect of RREB1 on cell proliferation in vivo by using a subcutaneous xenograft tumor model in nude mice. Finally, by using Western blotting and IHC, we demonstrated the possible mechanism by which RREB1 affects cell proliferation. The IHC and bioinformatics analyses demonstrated that RREB1 was highly expressed in gastric cancer and showed that RREB1-expressing patients had a larger tumor size and more lymphovascular invasion than RREB1-negative patients. Knockdown of RREB1 inhibited cell proliferation in vivo and in vitro. Knockdown of RREB1 enhanced p16 expression in vivo and in vitro, and p16 expression was negatively related to RREB1 in gastric cancer tissue. RREB1 was highly expressed in gastric cancer, and knockdown of RREB1 inhibited cell proliferation via enhanced p16 expression.

Received signal strength- (RSS-) based localization has attracted considerable attention for its low cost and easy implementation. In plenty of existing work, sensor positions, which play an important role in source localization, are usually assumed perfectly known. Unfortunately, they are often subject to uncertainties, which directly leads to effect on localization result. To tackle this problem, we study the RSS-based source localization with sensor position uncertainty. Sensor position uncertainty will be modeled as two types: Gaussian random variable and unknown nonrandom variable. For either of the models, two semidefinite programming (SDP) methods are proposed, i.e., SDP-1 and SDP-2. The SDP-1 method proceeds from the nonconvex problem with respect to the maximum likelihood (ML) estimation and then obtains an SDP problem using proper approximation and relaxation. The SDP-2 method first transfers the sensor position uncertainties to the source position and then obtains an SDP formulation following a similar idea as in SDP-1 method. Numerical examples demonstrate the performance superiority of the proposed methods, compared to some existing methods assuming perfect sensor position information.

The popularity of higher education in China has significantly increased campus energy consumption. The main energy consumption is students’ use of electric water heaters (EWH)to heat household water on campuses. Analyzing and modeling students’ hot water use behavior will greatly help the research of campus water-saving and energy-saving strategies. Based on the measured data of a university in China, this study investigated the variation of students’ hot water use behavior varies by season, day of the week, time of year, and built a shower model. This study aims to determine the optimal heating strategy by using the CPLEX solver and considering the hot water supply mode of central heating distributed to each dormitory. This strategy increases consumer comfort while lowering electricity purchasing expenses for service providers.

Received signal strength- (RSS-) based localization in wireless sensor networks (WSNs) has attracted significant attention due to its advantages of low cost and simple implementation. In practice, RSS measurements may suffer from sensor biases, which deteriorates the localization accuracy. However, most of the existing localization methods are designed for bias-free measurements. In this paper, we propose a convex combination method for RSS localization in the presence of sensor biases. The parameter vector composed of unknown location and sensor biases is estimated simultaneously by using a convex combination of some virtual points. These virtual points form a convex hull, into which the parameter vector falls with large probability. By this, the original nonconvex estimation problem is converted to be convex. Numerical examples demonstrate the superiority of the proposed method in terms of localization accuracy, compared to the existing semidefinite programming (SDP) methods.